Libigl is an open source C++ library for geometry processing research and development. Dropping the heavy data structures of tradition geometry libraries, libigl is a simple header-only library of encapsulated functions. This combines the rapid prototyping familiar to Matlab or Python programmers with the performance and versatility of C++. The tutorial is a self-contained, hands-on introduction to libigl. Via interactive, step-by-step examples, we demonstrate how to accomplish common geometry processing tasks such as computation of differential quantities and operators, real-time deformation, parametrization, numerical optimization and remeshing. Each section of the lecture notes links to a cross-platform example application.

- Chapter 1: Introduction to libigl
- Chapter 2: Discrete Geometric Quantities and Operators
- Chapter 3: Matrices and Linear Algebra
- Chapter 4: Shape Deformation
- Chapter 5: Parametrization
- 501 Harmonic parametrization
- 502 Least-Square Conformal Maps
- 503 As-Rigid-As-Possible
- 504 N-Rotationally symmetric tangent fields
- 505 Global, seamless integer-grid parametrization
- 506 Anisotropic remeshing using frame fields
- 507 N-PolyVector fields
- 508 Conjugate vector fields
- 509 Planarization
- 510 Integrable PolyVector Fields
- 511 General N-PolyVector Fields

- Chapter 6: External libraries
- 601 State serialization
- 602 Mixing Matlab code
- 603 Calling libigl functions from Matlab
- 604 Triangulation of closed polygons
- 605 Tetrahedralization of closed surfaces
- 606 Baking ambient occlusion
- 607 Screen Capture
- 608 Locally Injective Maps
- 609 Boolean Operations on Meshes
- 610 CSG Tree
- 611 Scalable Locally Injective Maps

- Chapter 7: Miscellaneous
- Chapter 8: Outlook for continuing development

We introduce libigl with a series of self-contained examples. The purpose of each example is to showcase a feature of libigl while applying to a practical problem in geometry processing. In this chapter, we will present the basic concepts of libigl and introduce a simple mesh viewer that allows to visualize a surface mesh and its attributes. All the tutorial examples are cross-platform and can be compiled on MacOSX, Linux and Windows.

Before getting into the examples, we summarize the main design principles in libigl:

**No complex data types.**We mostly use matrices and vectors. This greatly favors code reusability and forces the function authors to expose all the parameters used by the algorithm.**Minimal dependencies.**We use external libraries only when necessary and we wrap them in a small set of functions.**Header-only.**It is straight forward to use our library since it is only one additional include directory in your project. (if you are worried about compilation speed, it is also possible to build the library as a static library)**Function encapsulation.**Every function (including its full implementation) is contained in a pair of .h/.cpp files with the same name of the function.

libigl can be downloaded from our github repository or cloned with git:

```
git clone --recursive https://github.com/libigl/libigl.git
```

The core libigl functionality only depends on the C++ Standard Library and Eigen.

To build all the examples in the tutorial, you can use the CMakeLists.txt in the tutorial folder:

```
cd tutorial
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ../
make
```

The examples can also be built independently using the CMakeLists.txt inside each example folder.

*Note for linux users*: Many linux distributions do not include gcc and the basic development tools
in their default installation. On Ubuntu, you need to install the following packages:

```
sudo apt-get install git
sudo apt-get install build-essential
sudo apt-get install cmake
sudo apt-get install libx11-dev
sudo apt-get install mesa-common-dev libgl1-mesa-dev libglu1-mesa-dev
sudo apt-get install libxrandr-dev
sudo apt-get install libxi-dev
sudo apt-get install libxmu-dev
sudo apt-get install libblas-dev
```

*Note for windows users*: libigl only supports the Microsoft Visual Studio 2015 compiler in 64bit mode. It will not work with a 32bit build and it will not work
with older versions of visual studio.

A few examples in Chapter 5 requires the CoMiSo solver. We provide a mirror of CoMISo that works out of the box with libigl. To install it:

```
cd libigl/external
git clone --recursive https://github.com/libigl/CoMISo.git
```

You can then build the tutorials again and it libigl will automatically find and compile CoMISo.

*Note 1*: CoMISo is distributed under the GPL3 license, it does impose restrictions on commercial usage.

*Note 2*: CoMISo requires a blas implementation. We use the built-in blas in macosx and linux, and we bundle a precompiled binary for VS2015 64 bit. Do NOT compile the tutorials
in 32 bit on windows.

We provide a blank project example showing how to use libigl and cmake. Feel free and encouraged to copy or fork this project as a way of starting a new personal project using libigl.

libigl uses the Eigen library to encode vector and matrices. We suggest that you keep the dense and sparse quick reference guides at hand while you read the examples in this tutorial.

A triangular mesh is encoded as a pair of matrices:

```
Eigen::MatrixXd V;
Eigen::MatrixXi F;
```

`V`

is a #N by 3 matrix which stores the coordinates of the vertices. Each
row stores the coordinate of a vertex, with its x,y and z coordinates in the first,
second and third column, respectively. The matrix `F`

stores the triangle
connectivity: each line of `F`

denotes a triangle whose 3 vertices are
represented as indices pointing to rows of `V`

.

Note that the order of the vertex indices in `F`

determines the orientation of
the triangles and it should thus be consistent for the entire surface.
This simple representation has many advantages:

- it is memory efficient and cache friendly
- the use of indices instead of pointers greatly simplifies debugging
- the data can be trivially copied and serialized

libigl provides input [output] functions to read [write] many common mesh formats. The IO functions are contained in the files read*.h and write*.h. As a general rule each libigl function is contained in a pair of .h/.cpp files with the same name. By default, the .h files include the corresponding cpp files, making the library header-only.

Reading a mesh from a file requires a single libigl function call:

```
igl::readOFF(TUTORIAL_SHARED_PATH "/cube.off", V, F);
```

The function reads the mesh cube.off and it fills the provided `V`

and `F`

matrices.
Similarly, a mesh can be written in an OBJ file using:

```
igl::writeOBJ("cube.obj",V,F);
```

Example 101 contains a simple mesh converter from OFF to OBJ format.

Libigl provides an glfw-based OpenGL 3.2 viewer to visualize surfaces, their properties and additional debugging informations.

The following code (Example 102) is a basic skeleton for all the examples that will be used in the tutorial. It is a standalone application that loads a mesh and uses the viewer to render it.

```
#include <igl/readOFF.h>
#include <igl/viewer/Viewer.h>
Eigen::MatrixXd V;
Eigen::MatrixXi F;
int main(int argc, char *argv[])
{
// Load a mesh in OFF format
igl::readOFF(TUTORIAL_SHARED_PATH "/bunny.off", V, F);
// Plot the mesh
igl::viewer::Viewer viewer;
viewer.data.set_mesh(V, F);
viewer.launch();
}
```

The function `set_mesh`

copies the mesh into the viewer.
`Viewer.launch()`

creates a window, an OpenGL context and it starts the draw loop.
Additional properties can be plotted on the mesh (as we will see later),
and it is possible to extend the viewer with standard OpenGL code.
Please see the documentation in
Viewer.h for more details.

Keyboard and mouse events triggers callbacks that can be registered in the viewer. The viewer supports the following callbacks:

```
bool (*callback_pre_draw)(Viewer& viewer);
bool (*callback_post_draw)(Viewer& viewer);
bool (*callback_mouse_down)(Viewer& viewer, int button, int modifier);
bool (*callback_mouse_up)(Viewer& viewer, int button, int modifier);
bool (*callback_mouse_move)(Viewer& viewer, int mouse_x, int mouse_y);
bool (*callback_mouse_scroll)(Viewer& viewer, float delta_y);
bool (*callback_key_down)(Viewer& viewer, unsigned char key, int modifiers);
bool (*callback_key_up)(Viewer& viewer, unsigned char key, int modifiers);
```

A keyboard callback can be used to visualize multiple meshes or different stages of an algorithm, as demonstrated in Example 103, where the keyboard callback changes the visualized mesh depending on the key pressed:

```
bool key_down(igl::viewer::Viewer& viewer, unsigned char key, int modifier)
{
if (key == '1')
{
viewer.data.clear();
viewer.data.set_mesh(V1, F1);
viewer.core.align_camera_center(V1,F1);
}
else if (key == '2')
{
viewer.data.clear();
viewer.data.set_mesh(V2, F2);
viewer.core.align_camera_center(V2,F2);
}
return false;
}
```

The callback is registered in the viewer as follows:

```
viewer.callback_key_down = &key_down;
```

Note that the mesh is cleared before using set_mesh. This has to be called every time the number of vertices or faces of the plotted mesh changes. Every callback returns a boolean value that tells the viewer if the event has been handled by the plugin, or if the viewer should process it normally. This is useful, for example, to disable the default mouse event handling if you want to control the camera directly in your code.

The viewer can be extended using plugins, which are classes that implements all the viewer’s callbacks. See the Viewer_plugin for more details.

Colors and normals can be associated to faces or vertices using the set_colors function:

```
viewer.data.set_colors(C);
```

`C`

is a #C by 3 matrix with one RGB color per row. `C`

must have as many
rows as the number of faces **or** the number of vertices of the mesh.
Depending on the size of `C`

, the viewer applies the color to the faces or to
the vertices.

Colors can be used to visualize a scalar function defined on a surface. The
scalar function is converted to colors using a color transfer function, which
maps a scalar value between 0 and 1 to a color. A simple example of a scalar
field defined on a surface is the z coordinate of each point, which can be
extract from our mesh representation by taking the last column of `V`

(Example 104). The function `igl::jet`

can be used to
convert it to colors:

```
Eigen::VectorXd Z = V.col(2);
igl::jet(Z,true,C);
```

The first row extracts the third column from `V`

(the z coordinate of each
vertex) and the second calls a libigl functions that converts a scalar field to colors. The second parameter of jet normalizes the scalar field to lie between 0 and 1 before applying the transfer function.

`igl::jet`

is an example of a standard function in libigl: it takes simple
types and can be easily reused for many different tasks. Not committing to
heavy data structures types favors simplicity, ease of use and reusability.

In addition to plotting the surface, the viewer supports the visualization of points, lines and text labels: these overlays can be very helpful while developing geometric processing algorithms to plot debug informations.

```
viewer.data.add_points(P,Eigen::RowVector3d(r,g,b));
```

Draws a point of color r,g,b for each row of P. The point is placed at the coordinates specified in each row of P, which is a #P by 3 matrix.

```
viewer.data.add_edges(P1,P2,Eigen::RowVector3d(r,g,b);
```

Draws a line of color r,g,b for each row of P1 and P2, which connects the 3D point in to the point in P2. Both P1 and P2 are of size #P by 3.

```
viewer.data.add_label(p,str);
```

Draws a label containing the string str at the position p, which is a vector of length 3.

These functions are demonstrate in Example 105 where
the bounding box of a mesh is plotted using lines and points.
Using matrices to encode the mesh and its attributes allows to write short and
efficient code for many operations, avoiding to write for loops. For example,
the bounding box of a mesh can be found by taking the colwise maximum and minimum of `V`

:

```
Eigen::Vector3d m = V.colwise().minCoeff();
Eigen::Vector3d M = V.colwise().maxCoeff();
```

As of version 1.2 the viewer uses a new menu and completely replaces AntTweakBar. It is based on the open-source projects nanovg and nanogui. To extend the default menu of the viewer and to expose more user defined variables you have to define a callback function:

```
igl::viewer::Viewer viewer;
bool boolVariable = true;
float floatVariable = 0.1f;
enum Orientation { Up=0,Down,Left,Right } dir = Up;
// Extend viewer menu
viewer.callback_init = [&](igl::viewer::Viewer& viewer)
{
// Add new group
viewer.ngui->addGroup("New Group");
// Expose a variable directly ...
viewer.ngui->addVariable("float",floatVariable);
// Expose an enumaration type
viewer.ngui->addVariable<Orientation>("Direction",dir)->setItems({"Up","Down","Left","Right"});
// Add a button
viewer.ngui->addButton("Print Hello",[](){ std::cout << "Hello\n"; });
// call to generate menu
viewer.screen->performLayout();
return false;
};
// start viewer
viewer.launch();
```

If you need a separate new menu window use:

```
viewer.ngui->addWindow(Eigen::Vector2i(220,10),"New Window");
```

If you do not want to expose variables directly but rather use the get/set functionality:

```
// ... or using a custom callback
viewer.ngui->addVariable<bool>("bool",[&](bool val) {
boolVariable = val; // setter
},[&]() {
return boolVariable; // getter
});
```

This chapter illustrates a few discrete quantities that libigl can compute on a mesh and the libigl functions that construct popular discrete differential geometry operators. It also provides an introduction to basic drawing and coloring routines of our viewer.

Surface normals are a basic quantity necessary for rendering a surface. There are a variety of ways to compute and store normals on a triangle mesh. Example 201 demonstrates how to compute and visualize normals with libigl.

Normals are well defined on each triangle of a mesh as the vector orthogonal to triangle’s plane. These piecewise-constant normals produce piecewise-flat renderings: the surface appears non-smooth and reveals its underlying discretization.

Normals can be computed and stored on vertices, and interpolated in the interior of the triangles to produce smooth renderings (Phong shading). Most techniques for computing per-vertex normals take an average of incident face normals. The main difference between these techniques is their weighting scheme: Uniform weighting is heavily biased by the discretization choice, whereas area-based or angle-based weighting is more forgiving.

The typical half-edge style computation of area-based weights has this structure:

```
N.setZero(V.rows(),3);
for(int i : vertices)
{
for(face : incident_faces(i))
{
N.row(i) += face.area * face.normal;
}
}
N.rowwise().normalize();
```

At first glance, it might seem inefficient to loop over incident faces—and thus constructing the per-vertex normals— without using an half-edge data structure. However, per-vertex normals may be *throwing* each face normal to
running sums on its corner vertices:

```
N.setZero(V.rows(),3);
for(int f = 0; f < F.rows();f++)
{
for(int c = 0; c < 3;c++)
{
N.row(F(f,c)) += area(f) * face_normal.row(f);
}
}
N.rowwise().normalize();
```

Storing normals per-corner is an efficient and convenient way of supporting both smooth and sharp (e.g. creases and corners) rendering. This format is common to OpenGL and the .obj mesh file format. Often such normals are tuned by the mesh designer, but creases and corners can also be computed automatically. Libigl implements a simple scheme which computes corner normals as averages of normals of faces incident on the corresponding vertex which do not deviate by more than a specified dihedral angle (e.g. 20°).

Gaussian curvature on a continuous surface is defined as the product of the principal curvatures:

\(k_G = k_1 k_2.\)

As an *intrinsic* measure, it depends on the metric and
not the surface’s embedding.

Intuitively, Gaussian curvature tells how locally spherical or *elliptic* the
surface is ( \(k_G>0\) ), how locally saddle-shaped or *hyperbolic* the surface
is ( \(k_G<0\) ), or how locally cylindrical or *parabolic* ( \(k_G=0\) ) the
surface is.

In the discrete setting, one definition for a “discrete Gaussian curvature”
on a triangle mesh is via a vertex’s *angular deficit*:

\(k_G(v_i) = 2π - \sum\limits_{j\in N(i)}θ_{ij},\)

where \(N(i)\) are the triangles incident on vertex \(i\) and \(θ_{ij}\) is the angle at vertex \(i\) in triangle \(j\) [1].

Just like the continuous analog, our discrete Gaussian curvature reveals elliptic, hyperbolic and parabolic vertices on the domain, as demonstrated in Example 202.

The two principal curvatures \((k_1,k_2)\) at a point on a surface measure how much the surface bends in different directions. The directions of maximum and minimum (signed) bending are called principal directions and are always orthogonal.

Mean curvature is defined as the average of principal curvatures:

\(H = \frac{1}{2}(k_1 + k_2).\)

One way to extract mean curvature is by examining the Laplace-Beltrami operator applied to the surface positions. The result is a so-called mean-curvature normal:

\(-\Delta \mathbf{x} = H \mathbf{n}.\)

It is easy to compute this on a discrete triangle mesh in libigl using the cotangent Laplace-Beltrami operator [1].

```
#include <igl/cotmatrix.h>
#include <igl/massmatrix.h>
#include <igl/invert_diag.h>
...
MatrixXd HN;
SparseMatrix<double> L,M,Minv;
igl::cotmatrix(V,F,L);
igl::massmatrix(V,F,igl::MASSMATRIX_TYPE_VORONOI,M);
igl::invert_diag(M,Minv);
HN = -Minv*(L*V);
H = HN.rowwise().norm(); //up to sign
```

Combined with the angle defect definition of discrete Gaussian curvature, one can define principal curvatures and use least squares fitting to find directions [1].

Alternatively, a robust method for determining principal curvatures is via quadric fitting [2]. In the neighborhood around every vertex, a best-fit quadric is found and principal curvature values and directions are analytically computed on this quadric (Example 203).

Scalar functions on a surface can be discretized as a piecewise linear function with values defined at each mesh vertex:

\(f(\mathbf{x}) \approx \sum\limits_{i=1}^n \phi_i(\mathbf{x})\, f_i,\)

where \(\phi_i\) is a piecewise linear hat function defined by the mesh so that
for each triangle \(\phi_i\) is *the* linear function which is one only at
vertex \(i\) and zero at the other corners.

Thus gradients of such piecewise linear functions are simply sums of gradients of the hat functions:

\(\nabla f(\mathbf{x}) \approx \nabla \sum\limits_{i=1}^n \phi_i(\mathbf{x})\, f_i = \sum\limits_{i=1}^n \nabla \phi_i(\mathbf{x})\, f_i.\)

This reveals that the gradient is a linear function of the vector of \(f_i\)
values. Because the \(\phi_i\) are linear in each triangle, their gradients are
*constant* in each triangle. Thus our discrete gradient operator can be written
as a matrix multiplication taking vertex values to triangle values:

\(\nabla f \approx \mathbf{G}\,\mathbf{f},\)

where \(\mathbf{f}\) is \(n\times 1\) and \(\mathbf{G}\) is an \(md\times n\) sparse
matrix. This matrix \(\mathbf{G}\) can be derived geometrically, e.g.
[ch. 2, 3].
Libigl’s `grad`

function computes \(\mathbf{G}\) for
triangle and tetrahedral meshes (Example 204):

The discrete Laplacian is an essential geometry processing tool. Many interpretations and flavors of the Laplace and Laplace-Beltrami operator exist.

In open Euclidean space, the *Laplace* operator is the usual divergence of
gradient (or equivalently the Laplacian of a function is the trace of its
Hessian):

\(\Delta f = \frac{\partial^2 f}{\partial x^2} + \frac{\partial^2 f}{\partial y^2} + \frac{\partial^2 f}{\partial z^2}.\)

The *Laplace-Beltrami* operator generalizes this to surfaces.

When considering piecewise-linear functions on a triangle mesh, a discrete Laplacian may be derived in a variety of ways. The most popular in geometry processing is the so-called “cotangent Laplacian” \(\mathbf{L}\), arising simultaneously from FEM, DEC and applying divergence theorem to vertex one-rings. As a linear operator taking vertex values to vertex values, the Laplacian \(\mathbf{L}\) is a \(n\times n\) matrix with elements:

\(L_{ij} = \begin{cases}j \in N(i) &\cot \alpha_{ij} + \cot \beta_{ij},\\ j \notin N(i) & 0,\\ i = j & -\sum\limits_{k\neq i} L_{ik}, \end{cases}\)

where \(N(i)\) are the vertices adjacent to (neighboring) vertex \(i\), and \(\alpha_{ij},\beta_{ij}\) are the angles opposite to edge \({ij}\). This formula leads to a typical half-edge style implementation for constructing \(\mathbf{L}\):

```
for(int i : vertices)
{
for(int j : one_ring(i))
{
for(int k : triangle_on_edge(i,j))
{
L(i,j) = cot(angle(i,j,k));
L(i,i) -= cot(angle(i,j,k));
}
}
}
```

Similarly as before, it may seem to loop over one-rings without having an half-edge data structure. However, this is not the case, since the Laplacian may be built by summing together contributions for each triangle, much in spirit with its FEM discretization of the Dirichlet energy (sum of squared gradients):

```
for(triangle t : triangles)
{
for(edge i,j : t)
{
L(i,j) += cot(angle(i,j,k));
L(j,i) += cot(angle(i,j,k));
L(i,i) -= cot(angle(i,j,k));
L(j,j) -= cot(angle(i,j,k));
}
}
```

Libigl implements discrete “cotangent” Laplacians for triangles meshes and tetrahedral meshes, building both with fast geometric rules rather than “by the book” FEM construction which involves many (small) matrix inversions, cf. [4].

The operator applied to mesh vertex positions amounts to smoothing by *flowing*
the surface along the mean curvature normal direction (Example 205). Note that this is equivalent to minimizing surface area.

The mass matrix \(\mathbf{M}\) is another \(n \times n\) matrix which takes vertex values to vertex values. From an FEM point of view, it is a discretization of the inner-product: it accounts for the area around each vertex. Consequently, \(\mathbf{M}\) is often a diagonal matrix, such that \(M_{ii}\) is the barycentric or voronoi area around vertex \(i\) in the mesh [1]. The inverse of this matrix is also very useful as it transforms integrated quantities into point-wise quantities, e.g.:

\(\Delta f \approx \mathbf{M}^{-1} \mathbf{L} \mathbf{f}.\)

In general, when encountering squared quantities integrated over the surface, the mass matrix will be used as the discretization of the inner product when sampling function values at vertices:

\(\int_S x\, y\ dA \approx \mathbf{x}^T\mathbf{M}\,\mathbf{y}.\)

An alternative mass matrix \(\mathbf{T}\) is a \(md \times md\) matrix which takes triangle vector values to triangle vector values. This matrix represents an inner-product accounting for the area associated with each triangle (i.e. the triangles true area).

An alternative construction of the discrete cotangent Laplacian is by “squaring” the discrete gradient operator. This may be derived by applying Green’s identity (ignoring boundary conditions for the moment):

\(\int_S \|\nabla f\|^2 dA = \int_S f \Delta f dA\)

Or in matrix form which is immediately translatable to code:

\(\mathbf{f}^T \mathbf{G}^T \mathbf{T} \mathbf{G} \mathbf{f} = \mathbf{f}^T \mathbf{M} \mathbf{M}^{-1} \mathbf{L} \mathbf{f} = \mathbf{f}^T \mathbf{L} \mathbf{f}.\)

So we have that \(\mathbf{L} = \mathbf{G}^T \mathbf{T} \mathbf{G}\). This also
hints that we may consider \(\mathbf{G}^T\) as a discrete *divergence* operator,
since the Laplacian is the divergence of the gradient. Naturally, \(\mathbf{G}^T\) is
a \(n \times md\) sparse matrix which takes vector values stored at triangle faces
to scalar divergence values at vertices.

Libigl relies heavily on the Eigen library for dense and sparse linear algebra routines. Besides geometry processing routines, libigl has linear algebra routines which bootstrap Eigen and make it feel even more similar to a high-level algebra library such as Matlab.

A very familiar and powerful routine in Matlab is array slicing. This allows reading from or writing to a possibly non-contiguous sub-matrix. Let’s consider the Matlab code:

```
B = A(R,C);
```

If `A`

is a \(m \times n\) matrix and `R`

is a \(j\)-long list of row-indices
(between 1 and \(m\)) and `C`

is a \(k\)-long list of column-indices, then as a
result `B`

will be a \(j \times k\) matrix drawing elements from `A`

according to
`R`

and `C`

. In libigl, the same functionality is provided by the `slice`

function (Example 301):

```
VectorXi R,C;
MatrixXd A,B;
...
igl::slice(A,R,C,B);
```

Note that `A`

and `B`

could also be sparse matrices.

Similarly, consider the Matlab code:

```
A(R,C) = B;
```

Now, the selection is on the left-hand side so the \(j \times k\) matrix `B`

is
being *written into* the submatrix of `A`

determined by `R`

and `C`

. This
functionality is provided in libigl using `slice_into`

:

```
igl::slice_into(B,R,C,A);
```

Matlab and other higher-level languages make it very easy to extract indices of sorting and comparison routines. For example in Matlab, one can write:

```
[Y,I] = sort(X,1,'ascend');
```

so if `X`

is a \(m \times n\) matrix then `Y`

will also be an \(m \times n\) matrix
with entries sorted along dimension `1`

in `'ascend'`

ing order. The second
output `I`

is a \(m \times n\) matrix of indices such that ```
Y(i,j) =
X(I(i,j),j);
```

. That is, `I`

reveals how `X`

is sorted into `Y`

.

This same functionality is supported in libigl:

```
igl::sort(X,1,true,Y,I);
```

Similarly, sorting entire rows can be accomplished in Matlab using:

```
[Y,I] = sortrows(X,'ascend');
```

where now `I`

is a \(m\) vector of indices such that `Y = X(I,:)`

.

In libigl, this is supported with

```
igl::sortrows(X,true,Y,I);
```

where again `I`

reveals the index of sort so that it can be reproduced with
`igl::slice(X,I,1,Y)`

.

Analogous functions are available in libigl for: `max`

, `min`

, and `unique`

.

Libigl implements a variety of other routines with the same api and functionality as common Matlab functions.

Name | Description |
---|---|

`igl::any_of` |
Whether any elements are non-zero (true) |

`igl::cat` |
Concatenate two matrices (especially useful for dealing with Eigen sparse matrices) |

`igl::ceil` |
Round entries up to nearest integer |

`igl::cumsum` |
Cumulative sum of matrix elements |

`igl::colon` |
Act like Matlab’s `:` , similar to Eigen’s `LinSpaced` |

`igl::cross` |
Cross product per-row |

`igl::dot` |
dot product per-row |

`igl::find` |
Find subscripts of non-zero entries |

`igl::floor` |
Round entries down to nearest integer |

`igl::histc` |
Counting occurrences for building a histogram |

`igl::hsv_to_rgb` |
Convert HSV colors to RGB (cf. Matlab’s `hsv2rgb` ) |

`igl::intersect` |
Set intersection of matrix elements. |

`igl::isdiag` |
Determine whether matrix is diagonal |

`igl::jet` |
Quantized colors along the rainbow. |

`igl::median` |
Compute the median per column |

`igl::mode` |
Compute the mode per column |

`igl::null` |
Compute the null space basis of a matrix |

`igl::nchoosek` |
Compute all k-size combinations of n-long vector |

`igl::orth` |
Orthogonalization of a basis |

`igl::parula` |
Generate a quantized colormap from blue to yellow |

`igl::randperm` |
Generate a random permutation of [0,…,n–1] |

`igl::rgb_to_hsv` |
Convert RGB colors to HSV (cf. Matlab’s `rgb2hsv` ) |

`igl::setdiff` |
Set difference of matrix elements |

`igl::sort` |
Sort elements or rows of matrix |

`igl::speye` |
Identity as sparse matrix |

`igl::sum` |
Sum along columns or rows (of sparse matrix) |

`igl::unique` |
Extract unique elements or rows of matrix |

A common linear system in geometry processing is the Laplace equation:

\(∆z = 0\)

subject to some boundary conditions, for example Dirichlet boundary conditions (fixed value):

\(\left.z\right|_{\partial{S}} = z_{bc}\)

In the discrete setting, the linear system can be written as:

\(\mathbf{L} \mathbf{z} = \mathbf{0}\)

where \(\mathbf{L}\) is the \(n \times n\) discrete Laplacian and \(\mathbf{z}\) is a vector of per-vertex values. Most of \(\mathbf{z}\) correspond to interior vertices and are unknown, but some of \(\mathbf{z}\) represent values at boundary vertices. Their values are known so we may move their corresponding terms to the right-hand side.

Conceptually, this is very easy if we have sorted \(\mathbf{z}\) so that interior vertices come first and then boundary vertices:

\[\left(\begin{array}{cc} \mathbf{L}_{in,in} & \mathbf{L}_{in,b}\\ \mathbf{L}_{b,in} & \mathbf{L}_{b,b}\end{array}\right) \left(\begin{array}{c} \mathbf{z}_{in}\\ \mathbf{z}_{b}\end{array}\right) = \left(\begin{array}{c} \mathbf{0}_{in}\\ \mathbf{z}_{bc}\end{array}\right)\]

The bottom block of equations is no longer meaningful so we’ll only consider the top block:

\[\left(\begin{array}{cc} \mathbf{L}_{in,in} & \mathbf{L}_{in,b}\end{array}\right) \left(\begin{array}{c} \mathbf{z}_{in}\\ \mathbf{z}_{b}\end{array}\right) = \mathbf{0}_{in}\]

We can move the known values to the right-hand side:

\[\mathbf{L}_{in,in} \mathbf{z}_{in} = - \mathbf{L}_{in,b} \mathbf{z}_{b}\]

Finally we can solve this equation for the unknown values at interior vertices \(\mathbf{z}_{in}\).

However, our vertices will often not be sorted in this way. One option would be to sort `V`

,
then proceed as above and then *unsort* the solution `Z`

to match `V`

. However,
this solution is not very general.

With array slicing no explicit sort is needed. Instead we can *slice-out*
submatrix blocks (\(\mathbf{L}_{in,in}\), \(\mathbf{L}_{in,b}\), etc.) and follow
the linear algebra above directly. Then we can slice the solution *into* the
rows of `Z`

corresponding to the interior vertices (Example 303).

The same Laplace equation may be equivalently derived by minimizing Dirichlet energy subject to the same boundary conditions:

\(\mathop{\text{minimize }}_z \frac{1}{2}\int\limits_S \|\nabla z\|^2 dA\)

On our discrete mesh, recall that this becomes

\(\mathop{\text{minimize }}_\mathbf{z} \frac{1}{2}\mathbf{z}^T \mathbf{G}^T \mathbf{D} \mathbf{G} \mathbf{z} \rightarrow \mathop{\text{minimize }}_\mathbf{z} \mathbf{z}^T \mathbf{L} \mathbf{z}\)

The general problem of minimizing some energy over a mesh subject to fixed value boundary conditions is so wide spread that libigl has a dedicated api for solving such systems.

Let us consider a general quadratic minimization problem subject to different common constraints:

\[\mathop{\text{minimize }}_\mathbf{z} \frac{1}{2}\mathbf{z}^T \mathbf{Q} \mathbf{z} + \mathbf{z}^T \mathbf{B} + \text{constant},\]

subject to

\[\mathbf{z}_b = \mathbf{z}_{bc} \text{ and } \mathbf{A}_{eq} \mathbf{z} = \mathbf{B}_{eq},\]

where

- \(\mathbf{Q}\) is a (usually sparse) \(n \times n\) positive semi-definite matrix of quadratic coefficients (Hessian),
- \(\mathbf{B}\) is a \(n \times 1\) vector of linear coefficients,
- \(\mathbf{z}_b\) is a \(|b| \times 1\) portion of
\(\mathbf{z}\) corresponding to boundary or
*fixed*vertices, - \(\mathbf{z}_{bc}\) is a \(|b| \times 1\) vector of known values corresponding to \(\mathbf{z}_b\),
- \(\mathbf{A}_{eq}\) is a (usually sparse) \(m \times n\) matrix of linear equality constraint coefficients (one row per constraint), and
- \(\mathbf{B}_{eq}\) is a \(m \times 1\) vector of linear equality constraint right-hand side values.

This specification is overly general as we could write \(\mathbf{z}_b = \mathbf{z}_{bc}\) as rows of \(\mathbf{A}_{eq} \mathbf{z} = \mathbf{B}_{eq}\), but these fixed value constraints appear so often that they merit a dedicated place in the API.

In libigl, solving such quadratic optimization problems is split into two routines: precomputation and solve. Precomputation only depends on the quadratic coefficients, known value indices and linear constraint coefficients:

```
igl::min_quad_with_fixed_data mqwf;
igl::min_quad_with_fixed_precompute(Q,b,Aeq,true,mqwf);
```

The output is a struct `mqwf`

which contains the system matrix factorization
and is used during solving with arbitrary linear terms, known values, and
constraint in the right-hand sides:

```
igl::min_quad_with_fixed_solve(mqwf,B,bc,Beq,Z);
```

The output `Z`

is a \(n \times 1\) vector of solutions with fixed values
correctly placed to match the mesh vertices `V`

.

We saw above that `min_quad_with_fixed_*`

in libigl provides a compact way to
solve general quadratic programs. Let’s consider another example, this time
with active linear equality constraints. Specifically let’s solve the
`bi-Laplace equation`

or equivalently minimize the Laplace energy:

\[\Delta^2 z = 0 \leftrightarrow \mathop{\text{minimize }}\limits_z \frac{1}{2} \int\limits_S (\Delta z)^2 dA\]

subject to fixed value constraints and a linear equality constraint:

\(z_{a} = 1, z_{b} = -1\) and \(z_{c} = z_{d}\).

Notice that we can rewrite the last constraint in the familiar form from above:

\(z_{c} - z_{d} = 0.\)

Now we can assembly `Aeq`

as a \(1 \times n\) sparse matrix with a coefficient
\(1\) in the column corresponding to vertex \(c\) and a \(-1\) at \(d\). The right-hand
side `Beq`

is simply zero.

Internally, `min_quad_with_fixed_*`

solves using the Lagrange Multiplier
method. This method adds additional variables for each linear constraint (in
general a \(m \times 1\) vector of variables \(\lambda\)) and then solves the
saddle problem:

\[\mathop{\text{find saddle }}_{\mathbf{z},\lambda}\, \frac{1}{2}\mathbf{z}^T \mathbf{Q} \mathbf{z} + \mathbf{z}^T \mathbf{B} + \text{constant} + \lambda^T\left(\mathbf{A}_{eq} \mathbf{z} - \mathbf{B}_{eq}\right)\]

This can be rewritten in a more familiar form by stacking \(\mathbf{z}\) and \(\lambda\) into one \((m+n) \times 1\) vector of unknowns:

\[\mathop{\text{find saddle }}_{\mathbf{z},\lambda}\, \frac{1}{2} \left( \mathbf{z}^T \lambda^T \right) \left( \begin{array}{cc} \mathbf{Q} & \mathbf{A}_{eq}^T\\ \mathbf{A}_{eq} & 0 \end{array} \right) \left( \begin{array}{c} \mathbf{z}\\ \lambda \end{array} \right) + \left( \mathbf{z}^T \lambda^T \right) \left( \begin{array}{c} \mathbf{B}\\ -\mathbf{B}_{eq} \end{array} \right) + \text{constant}\]

Differentiating with respect to \(\left( \mathbf{z}^T \lambda^T \right)\) reveals
a linear system and we can solve for \(\mathbf{z}\) and \(\lambda\). The only
difference from the straight quadratic *minimization* system, is that this
saddle problem system will not be positive definite. Thus, we must use a
different factorization technique (LDLT rather than LLT): libigl’s
`min_quad_with_fixed_precompute`

automatically chooses the correct solver in
the presence of linear equality constraints (Example 304).

We can generalize the quadratic optimization in the previous section even more by allowing inequality constraints. Specifically box constraints (lower and upper bounds):

\(\mathbf{l} \le \mathbf{z} \le \mathbf{u},\)

where \(\mathbf{l},\mathbf{u}\) are \(n \times 1\) vectors of lower and upper bounds and general linear inequality constraints:

\(\mathbf{A}_{ieq} \mathbf{z} \le \mathbf{B}_{ieq},\)

where \(\mathbf{A}_{ieq}\) is a \(k \times n\) matrix of linear coefficients and \(\mathbf{B}_{ieq}\) is a \(k \times 1\) matrix of constraint right-hand sides.

Again, we are overly general as the box constraints could be written as rows of the linear inequality constraints, but bounds appear frequently enough to merit a dedicated api.

Libigl implements its own active set routine for solving *quadratric programs*
(QPs). This algorithm works by iteratively “activating” violated inequality
constraints by enforcing them as equalities and “deactivating” constraints
which are no longer needed.

After deciding which constraints are active at each iteration, the problem
reduces to a quadratic minimization subject to linear *equality* constraints,
and the method from the previous section is invoked. This is repeated until convergence.

Currently the implementation is efficient for box constraints and sparse non-overlapping linear inequality constraints.

Unlike alternative interior-point methods, the active set method benefits from a warm-start (initial guess for the solution vector \(\mathbf{z}\)).

```
igl::active_set_params as;
// Z is optional initial guess and output
igl::active_set(Q,B,b,bc,Aeq,Beq,Aieq,Bieq,lx,ux,as,Z);
```

Libigl has rudimentary support for extracting eigen pairs of a generalized eigen value problem:

\(Ax = \lambda B x\)

where \(A\) is a sparse symmetric matrix and \(B\) is a sparse positive definite
matrix. Most commonly in geometry processing, we let \(A=L\) the cotangent
Laplacian and \(B=M\) the per-vertex mass matrix (e.g. [7]).
Typically applications will make use of the *low frequency* eigen modes.
Analagous to the Fourier decomposition, a function \(f\) on a surface can be
represented via its spectral decomposition of the eigen modes of the
Laplace-Beltrami:

\(f = \sum\limits_{i=1}^\infty a_i \phi_i\)

where each \(\phi_i\) is an eigen function satisfying: \(\Delta \phi_i = \lambda_i \phi_i\) and \(a_i\) are scalar coefficients. For a discrete triangle mesh, a completely analogous decomposition exists, albeit with finite sum:

\(\mathbf{f} = \sum\limits_{i=1}^n a_i \phi_i\)

where now a column vector of values at vertices \(\mathbf{f} \in \mathcal{R}^n\) specifies a piecewise linear function and \(\phi_i \in \mathcal{R}^n\) is an eigen vector satisfying:

\(\mathbf{L} \phi_i = \lambda_i \mathbf{M} \phi_i\).

Note that Vallet & Levy [7] propose solving a symmetrized
*standard* eigen problem \(\mathbf{M}^{-1/2}\mathbf{L}\mathbf{M}^{-1/2} \phi_i
= \lambda_i \phi_i\). Libigl implements a generalized eigen problem solver so
this unnecessary symmetrization can be avoided.

Often the sum above is *truncated* to the first \(k\) eigen vectors. If the low
frequency modes are chosen, i.e. those corresponding to small \(\lambda_i\)
values, then this truncation effectively *regularizes* \(\mathbf{f}\) to smooth,
slowly changing functions over the mesh (e.g. [8]). Modal
analysis and model subspaces have been used frequently in real-time deformation
(e.g. [9]).

In Example 306), the first 5 eigen vectors
of the discrete Laplace-Beltrami operator are computed and displayed in
pseudo-color atop the beetle. Eigen vectors are computed using `igl::eigs`

(mirroring MATLAB’s `eigs`

). The 5 eigen vectors are placed into the columns
of `U`

and the eigen values are placed into the entries of `S`

:

```
SparseMatrix<double> L,M;
igl::cotmatrix(V,F,L);
igl::massmatrix(V,F,igl::MASSMATRIX_TYPE_DEFAULT,M);
Eigen::MatrixXd U;
Eigen::VectorXd S;
igl::eigs(L,M,5,igl::EIGS_TYPE_SM,U,S);
```

Modern mesh-based shape deformation methods satisfy user deformation
constraints at handles (selected vertices or regions on the mesh) and propagate
these handle deformations to the rest of shape *smoothly* and *without removing
or distorting details*. Libigl provides implementations of a variety of
state-of-the-art deformation techniques, ranging from quadratic mesh-based
energy minimizers, to skinning methods, to non-linear elasticity-inspired
techniques.

The period of research between 2000 and 2010 produced a collection of techniques that cast the problem of handle-based shape deformation as a quadratic energy minimization problem or equivalently the solution to a linear partial differential equation.

There are many flavors of these techniques, but a prototypical subset are those that consider solutions to the bi-Laplace equation, that is a biharmonic function [10]. This fourth-order PDE provides sufficient flexibility in boundary conditions to ensure \(C^1\) continuity at handle constraints (in the limit under refinement) [11].

Let us first begin our discussion of biharmonic *deformation*, by considering
biharmonic *surfaces*. We will casually define biharmonic surfaces as surface
whose *position functions* are biharmonic with respect to some initial
parameterization:

\(\Delta^2 \mathbf{x}' = 0\)

and subject to some handle constraints, conceptualized as “boundary conditions”:

\(\mathbf{x}'_{b} = \mathbf{x}_{bc}.\)

where \(\mathbf{x}'\) is the unknown 3D position of a point on the surface. So we are asking that the bi-Laplacian of each of spatial coordinate function to be zero.

In libigl, one can solve a biharmonic problem with `igl::harmonic`

and setting \(k=2\) (*bi*-harmonic):

```
// U_bc contains deformation of boundary vertices b
igl::harmonic(V,F,b,U_bc,2,U);
```

This produces a smooth surface that interpolates the handle constraints, but all
original details on the surface will be *smoothed away*. Most obviously, if the
original surface is not already biharmonic, then giving all handles the
identity deformation (keeping them at their rest positions) will **not**
reproduce the original surface. Rather, the result will be the biharmonic
surface that does interpolate those handle positions.

Thus, we may conclude that this is not an intuitive technique for shape deformation.

Now we know that one useful property for a deformation technique is “rest pose reproduction”: applying no deformation to the handles should apply no deformation to the shape.

To guarantee this by construction we can work with *deformation fields* (ie.
displacements)
\(\mathbf{d}\) rather
than directly with positions \(\mathbf{x}\). Then the deformed positions can be
recovered as

\(\mathbf{x}' = \mathbf{x}+\mathbf{d}.\)

A smooth deformation field \(\mathbf{d}\) which interpolates the deformation
fields of the handle constraints will impose a smooth deformed shape
\(\mathbf{x}'\). Naturally, we consider *biharmonic deformation fields*:

\(\Delta^2 \mathbf{d} = 0\)

subject to the same handle constraints, but rewritten in terms of their implied deformation field at the boundary (handles):

\(\mathbf{d}_b = \mathbf{x}_{bc} - \mathbf{x}_b.\)

Again we can use `igl::harmonic`

with \(k=2\), but this time solve for the
deformation field and then recover the deformed positions:

```
// U_bc contains deformation of boundary vertices b
D_bc = U_bc - igl::slice(V,b,1);
igl::harmonic(V,F,b,D_bc,2,D);
U = V+D;
```

Biharmonic functions (whether positions or displacements) are solutions to the bi-Laplace equation, but also minimizers of the “Laplacian energy”. For example, for displacements \(\mathbf{d}\), the energy reads

\(\int\limits_S \|\Delta \mathbf{d}\|^2 dA,\)

where we define \(\Delta \mathbf{d}\) to simply apply the Laplacian coordinate-wise.

By linearity of the Laplace(-Beltrami) operator we can reexpress this energy in terms of the original positions \(\mathbf{x}\) and the unknown positions \(\mathbf{x}' = \mathbf{x} - \mathbf{d}\):

\(\int\limits_S \|\Delta (\mathbf{x}' - \mathbf{x})\|^2 dA = \int\limits_S \|\Delta \mathbf{x}' - \Delta \mathbf{x})\|^2 dA.\)

In the early work of Sorkine et al., the quantities \(\Delta \mathbf{x}'\) and \(\Delta \mathbf{x}\) were dubbed “differential coordinates” [12]. Their deformations (without linearized rotations) is thus equivalent to biharmonic deformation fields.

We can generalize biharmonic deformation by considering different powers of the Laplacian, resulting in a series of PDEs of the form:

\(\Delta^k \mathbf{d} = 0.\)

with \(k\in{1,2,3,\dots}\). The choice of \(k\) determines the level of continuity at the handles. In particular, \(k=1\) implies \(C^0\) at the boundary, \(k=2\) implies \(C^1\), \(k=3\) implies \(C^2\) and in general \(k\) implies \(C^{k-1}\).

```
int k = 2;// or 1,3,4,...
igl::harmonic(V,F,b,bc,k,Z);
```

In computer animation, shape deformation is often referred to as “skinning”. Constraints are posed as relative rotations of internal rigid “bones” inside a character. The deformation method, or skinning method, determines how the surface of the character (i.e. its skin) should move as a function of the bone rotations.

The most popular technique is linear blend skinning. Each point on the shape computes its new location as a linear combination of bone transformations:

\(\mathbf{x}' = \sum\limits_{i = 1}^m w_i(\mathbf{x}) \mathbf{T}_i \left(\begin{array}{c}\mathbf{x}_i\\1\end{array}\right),\)

where \(w_i(\mathbf{x})\) is the scalar *weight function* of the ith bone evaluated at
\(\mathbf{x}\) and \(\mathbf{T}_i\) is the bone transformation as a \(4 \times 3\)
matrix.

This formula is embarassingly parallel (computation at one point does not
depend on shared data need by computation at another point). It is often
implemented as a vertex shader. The weights and rest positions for each vertex
are sent as vertex shader *attributes* and bone transformations are sent as
*uniforms*. Then vertices are transformed within the vertex shader, just in
time for rendering.

As the skinning formula is linear (hence its name), we can write it as matrix multiplication:

\(\mathbf{X}' = \mathbf{M} \mathbf{T},\)

where \(\mathbf{X}'\) is \(n \times 3\) stack of deformed positions as row vectors, \(\mathbf{M}\) is a \(n \times m\cdot dim\) matrix containing weights and rest positions and \(\mathbf{T}\) is a \(m\cdot (dim+1) \times dim\) stack of transposed bone transformations.

Traditionally, the weight functions \(w_j\) are painted manually by skilled rigging professionals. Modern techniques now exist to compute weight functions automatically given the shape and a description of the skeleton (or in general any handle structure such as a cage, collection of points, selected regions, etc.).

Bounded biharmonic weights are one such technique that casts weight computation as a constrained optimization problem [13]. The weights enforce smoothness by minimizing the familiar Laplacian energy:

\(\sum\limits_{i = 1}^m \int_S (\Delta w_i)^2 dA\)

subject to constraints which enforce interpolation of handle constraints:

\(w_i(\mathbf{x}) = \begin{cases} 1 & \text{ if } \mathbf{x} \in H_i\\ 0 & \text{ otherwise } \end{cases},\)

where \(H_i\) is the ith handle, and constraints which enforce non-negativity, parition of unity and encourage sparsity:

\(0\le w_i \le 1\) and \(\sum\limits_{i=1}^m w_i = 1.\)

This is a quadratic programming problem and libigl solves it using its active set solver or by calling out to Mosek.

Even with high quality weights, linear blend skinning is limited. In particular, it suffers from known artifacts stemming from blending rotations as as matrices: a weight combination of rotation matrices is not necessarily a rotation. Consider an equal blend between rotating by \(-\pi/2\) and by \(\pi/2\) about the \(z\)-axis. Intuitively one might expect to get the identity matrix, but instead the blend is a degenerate matrix scaling the \(x\) and \(y\) coordinates by zero:

\(0.5\left(\begin{array}{ccc}0&-1&0\\1&0&0\\0&0&1\end{array}\right)+ 0.5\left(\begin{array}{ccc}0&1&0\\-1&0&0\\0&0&1\end{array}\right)= \left(\begin{array}{ccc}0&0&0\\0&0&0\\0&0&1\end{array}\right)\)

In practice, this means the shape shrinks and collapses in regions where bone weights overlap: near joints.

Dual quaternion skinning presents a solution [14]. This method represents rigid transformations as a pair of unit quaternions, \(\hat{\mathbf{q}}\). The linear blend skinning formula is replaced with a linear blend of dual quaternions:

\(\mathbf{x}' = \cfrac{\sum\limits_{i=1}^m w_i(\mathbf{x})\hat{\mathbf{q}_i}} {\left\|\sum\limits_{i=1}^m w_i(\mathbf{x})\hat{\mathbf{q}_i}\right\|} \mathbf{x},\)

where \(\hat{\mathbf{q}_i}\) is the dual quaternion representation of the rigid transformation of bone \(i\). The normalization forces the result of the linear blending to again be a unit dual quaternion and thus also a rigid transformation.

Like linear blend skinning, dual quaternion skinning is best performed in the
vertex shader. The only difference being that bone transformations are sent as
dual quaternions rather than affine transformation matrices. Libigl supports
CPU-side dual quaternion skinning with the `igl::dqs`

function, which takes a
more traditional representation of rigid transformations as input and
internally converts to the dual quaternion representation before blending:

```
// vQ is a list of rotations as quaternions
// vT is a list of translations
igl::dqs(V,W,vQ,vT,U);
```

Skinning and other linear methods for deformation are inherently limited. Difficult arises especially when large rotations are imposed by the handle constraints.

In the context of energy-minimization approaches, the problem stems from comparing positions (our displacements) in the coordinate frame of the undeformed shape. These quadratic energies are at best invariant to global rotations of the entire shape, but not smoothly varying local rotations. Thus linear techniques will not produce non-trivial bending and twisting.

Furthermore, when considering solid shapes (e.g. discretized with tetrahedral meshes) linear methods struggle to maintain local volume, and they often suffer from shrinking and bulging artifacts.

Non-linear deformation techniques present a solution to these problems.
They work by comparing the deformation of a mesh
vertex to its rest position *rotated* to a new coordinate frame which best
matches the deformation. The non-linearity stems from the mutual dependence of
the deformation and the best-fit rotation. These techniques are often labeled
“as-rigid-as-possible” as they penalize the sum of all local deformations’
deviations from rotations.

To arrive at such an energy, let’s consider a simple per-triangle energy:

\(E_\text{linear}(\mathbf{X}') = \sum\limits_{t \in T} a_t \sum\limits_{\{i,j\} \in t} w_{ij} \left\| \left(\mathbf{x}'_i - \mathbf{x}'_j\right) - \left(\mathbf{x}_i - \mathbf{x}_j\right)\right\|^2\)

where \(\mathbf{X}'\) are the mesh’s unknown deformed vertex positions, \(t\) is a triangle in a list of triangles \(T\), \(a_t\) is the area of triangle \(t\) and \(\{i,j\}\) is an edge in triangle \(t\). Thus, this energy measures the norm of change between an edge vector in the original mesh \(\left(\mathbf{x}_i - \mathbf{x}_j\right)\) and the unknown mesh \(\left(\mathbf{x}'_i - \mathbf{x}'_j\right)\).

This energy is **not** rotation invariant. If we rotate the mesh by 90 degrees
the change in edge vectors not aligned with the axis of rotation will be large,
despite the overall deformation being perfectly rigid.

So, the “as-rigid-as-possible” solution is to append auxiliary variables \(\mathbf{R}_t\) for each triangle \(t\) which are constrained to be rotations. Then the energy is rewritten, this time comparing deformed edge vectors to their rotated rest counterparts:

\(E_\text{arap}(\mathbf{X}',\{\mathbf{R}_1,\dots,\mathbf{R}_{|T|}\}) = \sum\limits_{t \in T} a_t \sum\limits_{\{i,j\} \in t} w_{ij} \left\| \left(\mathbf{x}'_i - \mathbf{x}'_j\right)- \mathbf{R}_t\left(\mathbf{x}_i - \mathbf{x}_j\right)\right\|^2.\)

The separation into the primary vertex position variables \(\mathbf{X}'\) and the
rotations \(\{\mathbf{R}_1,\dots,\mathbf{R}_{|T|}\}\) lead to strategy for
optimization, too. If the rotations \(\{\mathbf{R}_1,\dots,\mathbf{R}_{|T|}\}\)
are held fixed then the energy is quadratic in the remaining variables
\(\mathbf{X}'\) and can be optimized by solving a (sparse) global linear system.
Alternatively, if \(\mathbf{X}'\) are held fixed then each rotation is the
solution to a localized *Procrustes* problem (found via \(3 \times 3\) SVD or
polar decompostion). These two steps—local and global—each weakly decrease
the energy, thus we may safely iterate them until convergence.

The different flavors of “as-rigid-as-possible” depend on the dimension and codimension of the domain and the edge-sets \(T\). The proposed surface manipulation technique by Sorkine and Alexa [15], considers \(T\) to be the set of sets of edges emanating from each vertex (spokes). Later, Chao et al. derived the relationship between “as-rigid-as-possible” mesh energies and co-rotational elasticity considering 0-codimension elements as edge-sets: triangles in 2D and tetrahedra in 3D [16]. They also showed how Sorkine and Alexa’s edge-sets are not a discretization of a continuous energy, proposing instead edge-sets for surfaces containing all edges of elements incident on a vertex (spokes and rims). They show that this amounts to measuring bending, albeit in a discretization-dependent way.

Libigl, supports these common flavors. Selecting one is a matter of setting the energy type before the precompuation phase:

```
igl::ARAPData data;
arap_data.energy = igl::ARAP_ENERGY_TYPE_SPOKES;
//arap_data.energy = igl::ARAP_ENERGY_TYPE_SPOKES_AND_RIMS;
//arap_data.energy = igl::ARAP_ENERGY_TYPE_ELEMENTS; //triangles or tets
igl::arap_precomputation(V,F,dim,b,data);
```

Just like `igl::min_quad_with_fixed_*`

, this precomputation phase only depends
on the mesh, fixed vertex indices `b`

and the energy parameters. To solve with
certain constraints on the positions of vertices in `b`

, we may call:

```
igl::arap_solve(bc,data,U);
```

which uses `U`

as an initial guess and then computes the solution into it.

Libigl’s implementation of as-rigid-as-possible deformation takes advantage of the highly optimized singular value decomposition code from McAdams et al. [17] which leverages SSE intrinsics.

The concept of local rigidity will be revisited shortly in the context of surface parameterization.

Non-linear optimization is, unsurprisingly, slower than its linear cousins. In the case of the as-rigid-as-possible optimization, the bottleneck is typically the large number of polar decompositions necessary to recover best fit rotations for each edge-set (i.e. for each triangle, tetrahedron, or vertex cell). Even if this code is optimized, the number of primary degrees of freedom is tied to the discretization level, despite the deformations’ low frequency behavior.

This invites two routes toward fast non-linear optimization. First, is it necessary (or even advantageous) to find so many best-fit rotations? Second, can we reduce the degrees of freedom to better reflect the frequency of the desired deformations.

Taken in turn, these optimizations culminate in a method which optimizes over the space of linear blend skinning deformations spanned by high-quality weights (i.e. manually painted ones or bounded biharmonic weights). This space is a low-dimensional subspace of all possible mesh deformations, captured by writing linear blend skinning in matrix form:

\(\mathbf{X}' = \mathbf{M}\mathbf{T}\)

where the mesh vertex positions in the \(n \times 3\) matrix \(\mathbf{X}'\) are replaced by a linear combination of a small number of degrees of freedom in the \((3+1)m \times 3\) stack of transposed “handle” transformations. Swapping in \(\mathbf{M}\mathbf{T}\) for \(\mathbf{X}'\) in the ARAP energies above immediately sees performance gains during the global solve step as \(m << n\).

The complexity of the local step—fitting rotations—is still bound
to the original mesh discretization. However, if the skinning is well behaved,
we can make the assumption that places on the shape with similar skinning
weights will deform similarly and thus imply similar best-fit rotations.
Therefore, we cluster edge-sets according to their representation in
*weight-space*: where a vertex \(\mathbf{x}\) takes the coordinates
\([w_1(\mathbf{x}),w_2(\mathbf{x}),\dots,w_m(\mathbf{x})]\). The number of
clustered edge-sets show diminishing returns on the deformation quality so we
may choose a small number of clusters, proportional to the number of skinning
weight functions (rather than the number of discrete mesh vertices).

This proposed deformation model [18], can simultaneously be seen as a
fast, subspace optimization for ARAP and as an automatic method for finding
*the best* skinning transformation degrees of freedom.

A variety of user interfaces are supported via linear equality constraints on the skinning transformations associated with handles. To fix a transformation entirely we simply add the constraint:

\(\left(\begin{array}{cccc} 1 & 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ 0 & 0 & 1 & 0\\ 0 & 0 & 0 & 1\end{array}\right) \mathbf{T}_i^T = \hat{\mathbf{T}}_i^T,\)

where \(\hat{\mathbf{T}}_i^T\) is the \((3+1) \times 3\) transposed fixed transformation for handle \(i\).

To fix only the origin of a handle, we add a constraint requiring the transformation to interpolate a point in space (typically the centroid of all points with \(w_i = 1\):

\(\mathbf{c}'^T\mathbf{T}_i^T = \mathbf{c}^T,\)

where \(\mathbf{c}^T\) is the \(1 \times (3+1)\) position of the point at rest in transposed homogeneous coordinates, and \(\mathbf{c}'^T\) the point given by the user.

We can similarly fix just the linear part of the transformation at a handle, freeing the translation component (producing a “chickenhead” effect):

\(\left(\begin{array}{cccc} 1&0&0&0\\ 0&1&0&0\\ 0&0&1&0\end{array}\right) \mathbf{T}_i^T = \hat{\mathbf{L}}_i^T,\)

where \(\hat{\mathbf{L}}_i^T\) is the fixed \(3 \times 3\) linear part of the transformation at handle \(i\).

And lastly we can allow the user to entirely *free* the transformation’s
degrees of freedom, delegating the optimization to find the best possible
values for all elements. To do this, we simply abstain from adding a
corresponding constraint.

Being a subspace method, an immediate disadvantage is the reduced degrees of freedom. This brings performance, but in some situations limits behavior too much. In such cases one can use the skinning subspace to build an effective clustering of rotation edge-sets for a traditional ARAP optimization: forgoing the subspace substitution. This has an two-fold effect. The cost of the rotation fitting, local step drastically reduces, and the deformations are “regularized” according the clusters. From a high level point of view, if the clusters are derived from skinning weights, then they will discourage bending, especially along isolines of the weight functions. If handles are not known in advance, one could also cluster according to a “geodesic embedding” like the biharmonic distance embedding.

In this light, we can think of the “spokes+rims” style surface ARAP as a (slight and redundant) clustering of the per-triangle edge-sets.

Linear blend skinning (as above) deforms a mesh by
propogating *full affine transformations* at handles (bones, points, regions,
etc.) to the rest of the shape via weights. Another deformation framework,
called “generalized barycentric coordinates”, is a special case of linear blend
skinning [19]: transformations are restricted to
*pure translations* and weights are required to retain *affine precision*. This
latter requirement means that we can write the rest-position of any vertex in
the mesh as the weighted combination of the control handle locations:

\(\mathbf{x} = \sum\limits_{i=1}^m w_i(\mathbf{x}) * \mathbf{c}_i,\)

where \(\mathbf{c}_i\) is the rest position of the $i$th control point. This
simplifies the deformation formula at run-time. We can simply take the new
position of each point of the shape to be the weighted combination of the
*translated* control point positions:

\(\mathbf{x}' = \sum\limits_{i=1}^m w_i(\mathbf{x}) * \mathbf{c}_i'.\)

There are *many* different flavors of “generalized barycentric coordinates”
(see table in “Automatic Methods” section,
[19]). The vague goal of “generalized barycentric
coordinates” is to capture as many properties of simplicial barycentric
coordinates (e.g. for triangles in 2D and tetrahedral in 3D) for larger sets of
points or polyhedra. Some generalized barycentric coordinates can be computed
in closed form; others require optimization-based precomputation. Nearly all
flavors require connectivity information describing how the control points form
a external polyhedron around the input shape: a cage. However, a recent
techinique does not require a cage [20]. This method ensures
affine precision during optimization over weights of a smoothness energy with
affine functions in its kernel:

\(\mathop{\text{min}}_\mathbf{W}\,\, \text{trace}(\frac{1}{2}\mathbf{W}^T \mathbf{A} \mathbf{W}), \text{subject to: } \mathbf{C} = \mathbf{W}\mathbf{C}\)

subject to interpolation constraints at selected vertices. If \(\mathbf{A}\) has affine functions in its kernel—that is, if \(\mathbf{A}\mathbf{V} = 0\)—then the weights \(\mathbf{W}\) will retain affine precision and we’ll have that:

\(\mathbf{V} = \mathbf{W}\mathbf{C}\)

the matrix form of the equality above. The proposed way to define \(\mathbf{A}\)
is to construct a matrix \(\mathbf{K}\) that measures the Laplacian at all
interior vertices *and at all boundary vertices*. The *usual* definition of the
discrete Laplacian (e.g. what libigl returns from `igl::cotmatrix`

), measures
the Laplacian of a function for interior vertices, but measures the Laplacian
of a function *minus* the normal derivative of a function for boundary
vertices. Thus, we can let:

\(\mathbf{K} = \mathbf{L} + \mathbf{N}\)

where \(\mathbf{L}\) is the *usual* Laplacian and \(\mathbf{N}\) is matrix that
computes normal derivatives of a piecewise-linear function at boundary vertices
of a mesh. Then \(\mathbf{A}\) is taken as quadratic form computing the square of
the integral-average of \(\mathbf{K}\) applied to a function and integrated over
the mesh:

\(\mathbf{A} = (\mathbf{M}^{-1}\mathbf{K})^2_\mathbf{M} = \mathbf{K}^T \mathbf{M}^{-1} \mathbf{K}.\)

Since the Laplacian \(\mathbf{K}\) is a second-order derivative it measures zero on affine functions, thus \(\mathbf{A}\) has affine functions in its null space. A short derivation proves that this implies \(\mathbf{W}\) will be affine precise (see [20]).

Minimizers of this “squared Laplacian” energy are in some sense *discrete
biharmonic functions*. Thus they’re dubbed “biharmonic coordinates” (not the
same as *bounded biharmonic weights*, which are *not* generalized barycentric
coordinates).

In libigl, one can compute biharmonic coordinates given a mesh `(V,F)`

and a
list `S`

of selected control points or control regions (which act like skinning
handles):

```
igl::biharmonic_coordinates(V,F,S,W);
```

In computer graphics, we denote as surface parametrization a map from the
surface to \(\mathbf{R}^2\). It is usually encoded by a new set of 2D
coordinates for each vertex of the mesh (and possibly also by a new set of
faces in one to one correspondence with the faces of the original surface).
Note that
this definition is the *inverse* of the classical differential geometry
definition.

A parametrization has many applications, ranging from texture mapping to surface remeshing. Many algorithms have been proposed, and they can be broadly divided in four families:

**Single patch, fixed boundary**: these algorithm can parametrize a disk-like part of the surface given fixed 2D positions for its boundary. These algorithms are efficient and simple, but they usually produce high-distortion maps due to the fixed boundary.**Single patch, free boundary:**these algorithms let the boundary deform freely, greatly reducing the map distortion. Care should be taken to prevent the border to self-intersect.**Global parametrization**: these algorithms work on meshes with arbitrary genus. They initially cut the mesh in multiple patches that can be separately parametrized. The generated maps are discontinuous on the cuts (often referred as*seams*).**Global seamless parametrization**: these are global parametrization algorithm that hides the seams, making the parametrization “continuous”, under specific assumptions that we will discuss later.

Harmonic parametrization [21] is a single patch, fixed boundary parametrization algorithm that computes the 2D coordinates of the flattened mesh as two harmonic functions.

The algorithm is divided in 3 steps:

- Detect of the boundary vertices
- Map the boundary vertices to a circle
- Compute two harmonic functions (one for u and one for the v coordinate). The harmonic functions use the fixed vertices on the circle as boundary constraints.

The algorithm can be coded using libigl as follows:

```
Eigen::VectorXi bnd;
igl::boundary_loop(V,F,bnd);
Eigen::MatrixXd bnd_uv;
igl::map_vertices_to_circle(V,bnd,bnd_uv);
igl::harmonic(V,F,bnd,bnd_uv,1,V_uv);
```

where `bnd`

contains the indices of the boundary vertices, bnd_uv their position on the UV plane, and “1” denotes that we want to compute an harmonic function (2 will be for biharmonic, 3 for triharmonic, etc.). Note that each of the three
functions is designed to be reusable in other parametrization algorithms.

A UV parametrization can be visualized in the viewer with:

```
viewer.data.set_uv(V_uv);
```

The UV coordinates are then used to apply a procedural checkerboard texture to the mesh (Example 501).

Least squares conformal maps parametrization [22] minimizes the conformal (angular) distortion of the parametrization. Differently from harmonic parametrization, it does not need to have a fixed boundary.

LSCM minimizes the following energy:

\[ E_{LSCM}(\mathbf{u},\mathbf{v}) = \int_X \frac{1}{2}| \nabla \mathbf{u}^{\perp} - \nabla \mathbf{v} |^2 dA \]

which can be rewritten in matrix form as [23]:

\[ E_{LSCM}(\mathbf{u},\mathbf{v}) = \frac{1}{2} [\mathbf{u},\mathbf{v}]^t (L_c - 2A) [\mathbf{u},\mathbf{v}] \]

where \(L_c\) is the cotangent Laplacian matrix and \(A\) is a matrix such that \([\mathbf{u},\mathbf{v}]^t A [\mathbf{u},\mathbf{v}]\) is equal to the vector area of the mesh.

Using libigl, this matrix energy can be written in a few lines of code. The
cotangent matrix can be computed using `igl::cotmatrix`

:

```
SparseMatrix<double> L;
igl::cotmatrix(V,F,L);
```

Note that we want to apply the Laplacian matrix to the u and v coordinates at the same time, thus we need to extend it taking the left Kronecker product with a 2x2 identity matrix:

```
SparseMatrix<double> L_flat;
igl::repdiag(L,2,L_flat);
```

The area matrix is computed with `igl::vector_area_matrix`

:

```
SparseMatrix<double> A;
igl::vector_area_matrix(F,A);
```

The final energy matrix is \(L_{flat} - 2A\). Note that in this case we do not need to fix the boundary. To remove the null space of the energy and make the minimum unique, it is sufficient to fix two arbitrary vertices to two arbitrary positions. The full source code is provided in Example 502.

As-rigid-as-possible parametrization [24] is a powerful single-patch, non-linear algorithm to compute a parametrization that strives to preserve distances (and thus angles). The idea is very similar to ARAP surface deformation: each triangle is mapped to the plane trying to preserve its original shape, up to a rigid rotation.

The algorithm can be implemented reusing the functions discussed in the
deformation chapter: `igl::arap_precomputation`

and `igl::arap_solve`

. The only
difference is that the optimization has to be done in 2D instead of 3D and that
we need to compute a starting point. While for 3D deformation the optimization
is bootstrapped with the original mesh, this is not the case for ARAP
parametrization since the starting point must be a 2D mesh. In Example
503, we initialize the optimization with harmonic
parametrization. Similarly to LSCM, the boundary is free to deform to minimize
the distortion.

The design of tangent fields is a basic tool used to design guidance fields for uniform quadrilateral and hexahedral remeshing. Libigl contains an implementation of all the state-of-the-art algorithms to design N-RoSy fields and their generalizations.

In libigl, tangent unit-length vector fields are piece-wise constant on the faces of a triangle mesh, and they are described by one or more vectors per-face. The function

```
igl::nrosy(V,F,b,bc,b_soft,b_soft_weight,bc_soft,N,0.5,
output_field,output_singularities);
```

creates a smooth unit-length vector field (N=1) starting from a sparse set of constrained faces, whose indices are listed in b and their constrained value is specified in bc. The functions supports soft_constraints (b_soft, b_soft_weight, bc_soft), and returns the interpolated field for each face of the triangle mesh (output_field), plus the singularities of the field (output_singularities).

The singularities are vertices where the field vanishes (highlighted in red in
the figure above). `igl::nrosy`

can also generate N-RoSy fields [25],
which are a generalization of vector fields where in every face the vector is
defined up to a constant rotation of \(2\pi / N\). As can be observed in
the following figure, the singularities of the fields generated with different
N are of different types and they appear in different positions.

We demonstrate how to call and plot N-RoSy fields in Example
504, where the degree of the field can be change
pressing the number keys. `igl::nrosy`

implements the algorithm proposed in
[26]. N-RoSy fields can also be interpolated with the algorithm
proposed in [27], see Section npolyvectorfields for more details
(igl::n_polyvector).

The previous parametrization methods were focusing on creating parametrizations of surface patches aimed at texture mapping or baking of other surface properties such as normals and high-frequency details. Global, seamless parametrization aims at parametrizing complex shapes with a parametrization that is aligned with a given set of directions for the purpose of surface remeshing. In libigl, we provide a reference implementation of the pipeline proposed in the mixed integer quadrangulation paper [26].

The first step involves the design of a 4-RoSy field (sometimes called *cross*
field) that describes the alignment of the edges of the desired quadrilateral
remeshing. The field constraints are usually manually specified or extracted
from the principal curvature directions. In [Example
506], we simply fix one face in a random direction.

Given the cross field, we now want to cut the surface so that it becomes homeomorphic to a disk. While this could be done directly on the cross-field, we opt to perform this operation on its bisector field (a copy of the field rotated by 45 degrees) since it is more stable and generic. Working on the bisectors allow us to take as input generalized, non-orthogonal and non-unit length cross fields.

We thus rotate the field,

and we remove the rotation ambiguity by assigning to each face a u and a v direction. The assignment is done with a breadth-first search starting from a random face.

You can imagine this process as combing an hairy surface: you will be able to comb part of it, but at some point you will not be able to consistently comb the entire surface (Hairy ball theorem). The discontinuities in the combing define the cut graph:

Finally, we rotate the combed field by 45 degrees to undo the initial degrees rotation:

The combed cross field can be seen as the ideal Jacobian of the parametrization that will be computed in the next section.

The mesh is cut along the seams and a parametrization is computed trying to find two scalar functions whose gradient matches the combed cross field directions. This is a classical Poisson problem, that is solved minimizing the following quadratic energy:

\[ E(\mathbf{u},\mathbf{v}) = |\nabla \mathbf{u} - X_u|^2 + |\nabla \mathbf{v} - X_v|^2 \]

where \(X_u\) and \(X_u\) denotes the combed cross field. Solving this problem generates a parametrization whose u and v isolines are aligned with the input cross field.

We hide the seams by adding integer constraints to the Poisson problem that align the isolines on both sides of each seam [26].

Note that this parametrization can only be used for remeshing purposes, since it contains many overlaps.

A quad mesh can be extracted from this parametrization using libQEx (not included in libigl). The full pipeline is implemented in Example 505.

Anisotropic and non-uniform quad remeshing is important to concentrate the elements in the regions with more details. It is possible to extend the MIQ quad meshing framework to generate anisotropic quad meshes using a mesh deformation approach [28].

The input of the anisotropic remeshing algorithm is a sparse set of constraints
that define the shape and scale of the desired quads. This can be encoded as a
frame field, which is a pair of non-orthogonal and non-unit length vectors. The
frame field can be interpolated by decomposing it in a 4-RoSy field and a
unique affine transformation. The two parts can then be interpolated
separately, using `igl::nrosy`

for the cross field, and an harmonic interpolant
for the affine part.

After the interpolation, the surface is warped to transform each frame into an orthogonal and unit length cross (i.e. removing the scaling and skewness from the frame). This deformation defines a new embedding (and a new metric) for the surface.

The deformed surface can the be isotropically remeshed using the MIQ algorithm that has been presented in the previous section.

The UV coordinates of the deformed surface can then be used to transport the parametrization to the original surface, where the isolines will trace a quad mesh whose elements are similar to the shape prescribed in the input frame field.

Our implementation (Example 506) uses MIQ to generate the UV parametrization, but other algorithms could be applied: the only desiderata is that the generated quad mesh should be as isotropic as possible.

N-RoSy vector fields can be further generalized to represent arbitrary
vector-sets, with arbitrary angles between them and with arbitrary lengths
[29]. This generalization is called N-PolyVector field, and
libigl provides the function `igl::n_polyvector`

to design them starting from a
sparse set of constraints (Example 507).

The core idea is to represent the vector set as the roots of a complex polynomial: The polynomial coefficients are then harmonically interpolated leading to polynomials whose roots smoothly vary over the surface.

Globally optimal direction fields [27] are a special case of
PolyVector fields. If the constraints are taken from an N-RoSy field,
`igl::n_polyvector`

generates a field that is equivalent, after normalization,
to a globally optimal direction field.

Two tangent vectors lying on a face of a triangle mesh are conjugate if

\[ k_1 (u^T d_1)(v^T d_1) + k_2(u^T d_2)(v^T d_2) = 0. \]

This condition is very important in architectural geometry: The faces of an infinitely dense quad mesh whose edges are aligned with a conjugate field are planar. Thus, a quad mesh whose edges follow a conjugate field are easier to planarize [30].

Finding a conjugate vector field that satisfies given directional constraints is a standard problem in architectural geometry, which can be tackled by deforming a Poly-Vector field to the closest conjugate field.

This algorithm [29] alternates a global step, which enforces smoothness, with a local step, that projects the field on every face to the closest conjugate field (Example 508).

A quad mesh can be transformed in a planar quad mesh with Shape-Up [31], a local/global approach that uses the global step to enforce surface continuity and the local step to enforce planarity.

Example 509 planarizes a quad mesh until it satisfies a user-given planarity threshold.

Vector-field guided surface parameterization is based on the idea of designing the gradients of the parameterization functions (which are tangent vector fields on the surface) instead of the functions themselves. Thus, vector-set fields (N-Rosy, frame fields, and polyvector fields) that are to be used for parameterization (and subsequent remeshing) need to be integrable: it must be possible to break them down into individual vector fields that are gradients of scalar functions. Fields obtained by most smoothness-based design methods (eg. [25], [27], [29], [26], [28]) do not have this property. In [32], a method for creating integrable polyvector fields was introduced. This method takes as input a given field and improves its integrability by removing the vector field curl, thus turning it into a gradient of a function (Example 510).

This method retains much of the core principles of the polyvector framework - it expresses the condition for zero discrete curl condition (which typically requires integers for the vector matchings) into a condition involving continuous variables only. This is done using coefficients of appropriately defined polynomials. The parameterizations generated by the resulting fields are exactly aligned to the field directions and contain no inverted triangles.

While mostly applicable for the design of symmetric fields (i.e. fields that comprise of vector sets with symmetries between them at each point, e.g. N-RoSy or frame-fields), the framework presented in [29] can be used to design completely general fields, with possibly no such symmetries. For example, one can design fields that at each point comprise of an arbitrary number of vectors, not required to be collinear - as opposed e.g. to the case of the 4 pairwise-collinear vectors designed in the example (Example 507). This capability is implemented in the function igl::n_polyvector_general, and is illustrated in the example (Example 511).

The design of these general directional fields (also called vector-set fields) is based on the same polynomial framework and includes the symmetric fields as a special case. Note that in the case that some symmetries do exist in the constraints, the final field is not guaranteed to have these symmetries everywhere else on the mesh. For example, designing a field with 3 vectors per point where, at the constrained faces, two of the vectors are on a line opposite to each other, we are not guaranteed to always have two pairwise-collinear vectors everywhere in the result, as can be seen in the picture. In some cases however (as is the case of the frame field in the previous example Example 507) these symmetries are in fact guaranteed due to the particular nature of the polynomial that applies in that case (two coefficients are 0).

For a complete categorization of fields used in various applications (including these general ones) see Vaxman et al. 2016 [33].

An additional positive side effect of using matrices as basic types is that it is easy to exchange data between libigl and other softwares and libraries.

Geometry processing applications often require a considerable amount of computational time and/or manual input. Serializing the state of the application is a simple strategy to greatly increase the development efficiency. It allows to quickly start debugging just before the crash happens, avoiding to wait for the precomputation to take place every time and it also makes your experiments reproducible, allowing to quickly test algorithms variants on the same input data.

Serialization is often not considered in geometry processing due to the extreme difficulty in serializing pointer-based data structured, such as an half-edge data structure (OpenMesh, CGAL), or a pointer based indexed structure (VCG).

In libigl, serialization is much simpler, since the majority of the functions use basic types, and pointers are used in very rare cases (usually to interface with external libraries). Libigl bundles a simple and self-contained binary and XML serialization framework, that drastically reduces the overhead required to add serialization to your applications.

To de-/serialize a set of variables use the following method:

```
#include "igl/serialize.h"
bool b = true;
unsigned int num = 10;
std::vector<float> vec = {0.1,0.002,5.3};
// use overwrite = true for the first serialization to create or overwrite an existing file
igl::serialize(b,"B","filename",true);
// append following serialization to existing file
igl::serialize(num,"Number","filename");
igl::serialize(vec,"VectorName","filename");
// deserialize back to variables
igl::deserialize(b,"B","filename");
igl::deserialize(num,"Number","filename");
igl::deserialize(vec,"VectorName","filename");
```

Currently all fundamental data types (bool, int, float, double, …) are supported, as well as std::string, basic `STL`

containers, dense and sparse Eigen matrices and nestings of those.
Some limitations apply to pointers. Currently, loops or many to one type of link structures are not handled correctly. Each pointer is assumed to point to a different independent object.
Uninitialized pointers must be set to `nullptr`

before de-/serialization to avoid memory leaks. Cross-platform issues like little-, big-endianess is currently not supported.
To make user defined types serializable, just derive from `igl::Serializable`

and trivially implementing the `InitSerialization`

method.

Assume that the state of your application is a mesh and a set of integer ids:

```
#include "igl/serialize.h"
struct State : public igl::Serializable
{
Eigen::MatrixXd V;
Eigen::MatrixXi F;
std::vector<int> ids;
void InitSerialization()
{
this->Add(V , "V");
this->Add(F , "F");
this->Add(ids, "ids");
}
};
```

If you need more control over the serialization of your types, you can override the following functions or directly inherit from the interface `igl::SerializableBase`

.

```
bool Serializable::PreSerialization() const;
void Serializable::PostSerialization() const;
bool Serializable::PreDeserialization();
void Serializable::PostDeserialization();
```

Alternatively, if you want a non-intrusive way of serializing your state you can overload the following functions:

```
namespace igl
{
namespace serialization
{
template <> inline void serialize(const State& obj,std::vector<char>& buffer){
::igl::serialize(obj.V,std::string("V"),buffer);
::igl::serialize(obj.F,std::string("F"),buffer);
::igl::serialize(obj.ids,std::string("ids"),buffer);
}
template <> inline void deserialize(State& obj,const std::vector<char>& buffer){
::igl::deserialize(obj.V,std::string("V"),buffer);
::igl::deserialize(obj.F,std::string("F"),buffer);
::igl::deserialize(obj.ids,std::string("ids"),buffer);
}
}
}
```

Equivalently, you can use the following macros:

```
SERIALIZE_TYPE(State,
SERIALIZE_MEMBER(V)
SERIALIZE_MEMBER(F)
SERIALIZE_MEMBER_NAME(ids,"ids")
)
```

All the former code is for binary serialization which is especially useful if you have to handle larger data where the loading and saving times become more important.
For cases where you want to read and edit the serialized data by hand we provide a serialization to XML files which is based on the library tinyxml2.
There you also have the option to create a partial binary serialization of your data by using the binary parameter, exposed in the function `serialize_xml()`

:

```
#include "igl/xml/serialize_xml.h"
int number;
// binary = false, overwrite = true
igl::serialize_xml(vec,"VectorXML",xmlFile,false,true);
// binary = true, overwrite = true
igl::serialize_xml(vec,"VectorBin",xmlFile,true,true);
igl::deserialize_xml(vec,"VectorXML",xmlFile);
igl::deserialize_xml(vec,"VectorBin",xmlFile);
```

For user defined types derive from `XMLSerializable`

.

The code snippets above are extracted from Example 601. We strongly suggest that you make the entire state of your application always serializable since it will save you a lot of troubles when you will be preparing figures for a scientific report. It is very common to have to do small changes to figures, and being able to serialize the entire state just before you take screenshots will save you many painful hours before a submission deadline.

Libigl can be interfaced with Matlab to offload numerically heavy computation to a Matlab script. The major advantage of this approach is that you will be able to develop efficient and complex user-interfaces in C++, while exploting the syntax and fast protototyping features of matlab. In particular, the use of an external Matlab script in a libigl application allows to change the Matlab code while the C++ application is running, greatly increasing coding efficiency.

We demonstrate how to integrate Matlab in a libigl application in Example 602. The example uses Matlab to compute the Eigenfunctions of the discrete Laplacian operator, relying on libigl for mesh IO, visualization and for computing the Laplacian operator.

Libigl can connect to an existing instance of Matlab (or launching a new one on Linux/MacOSX) using:

```
igl::mlinit(&engine);
```

The cotangent Laplacian is computed using igl::cotmatrix and uploaded to the Matlab workspace:

```
igl::cotmatrix(V,F,L);
igl::mlsetmatrix(&engine,"L",L);
```

It is now possible to use any Matlab function on the data. For example, we can see the sparsity pattern of L using spy:

```
igl::mleval(&engine,"spy(L)");
```

The results of Matlab computations can be returned back to the C++ application

```
igl::mleval(&engine,"[EV,~] = eigs(-L,10,'sm')");
igl::mlgetmatrix(&engine,"EV",EV);
```

and plotted using the libigl viewer.

To aid debugging, libigl also supplies functions to write Matlab `.mat`

“Workspaces”. This C++ snippet saves a mesh and it’s sparse Laplacian matrix to
a file:

```
igl::readOFF(TUTORIAL_SHARED_PATH "/fertility.off", V, F);
igl::cotmatrix(V,F,L);
igl::MatlabWorkspace mw;
mw.save(V,"V");
mw.save_index(F,"F");
mw.save(L,"L");
mw.write("fertility.mat");
```

Then this workspace can be loaded into a Matlab IDE:

```
load fertility.mat
```

The `igl::MatlabWorkspace`

depends on Matlab libraries to compile and run,
but—in contrast to the engine routines above—will avoid launching a Matlab
instance upon execution.

Eigen supplies a sophisticated API for printing its matrix types to the screen. Libigl has wrapped up a particularly useful formatting which makes it simple to copy standard output from a C++ program into a Matlab IDE. The code:

```
igl::readOFF(TUTORIAL_SHARED_PATH "/2triangles.off", V, F);
igl::cotmatrix(V,F,L);
std::cout<<igl::matlab_format(V,"V")<<std::endl;
std::cout<<igl::matlab_format((F.array()+1).eval(),"F")<<std::endl;
std::cout<<igl::matlab_format(L,"L")<<std::endl;
```

produces the output:

```
V = [
0 0 0
1 0 0
1 1 1
2 1 0
];
F = [
1 2 3
2 4 3
];
LIJV = [
1 1 -0.7071067811865476
2 1 0.7071067811865475
3 1 1.570092458683775e-16
1 2 0.7071067811865475
2 2 -1.638010440969447
3 2 0.6422285251880865
4 2 0.2886751345948129
1 3 1.570092458683775e-16
2 3 0.6422285251880865
3 3 -0.9309036597828995
4 3 0.2886751345948129
2 4 0.2886751345948129
3 4 0.2886751345948129
4 4 -0.5773502691896258
];
L = sparse(LIJV(:,1),LIJV(:,2),LIJV(:,3));
```

which is easily copied and pasted into Matlab for debugging, etc.

It is also possible to call libigl functions from matlab, compiling them as MEX functions. This can be used to offload to C++ code the computationally intensive parts of a Matlab application.

We provide a wrapper for `igl::readOBJ`

in Example 603.
We plan to provide wrappers for all our functions in the future, if you are
interested in this feature (or if you want to help implementing it) please let
us know.

The generation of high-quality triangle and tetrahedral meshes is a very common task in geometry processing. We provide wrappers in libigl to triangle and Tetgen.

A triangle mesh with a given boundary can be created with:

```
igl::triangulate(V,E,H,V2,F2,"a0.005q");
```

where `E`

is a set of boundary edges (#E by 2), `H`

is a set of 2D positions of
points contained in holes of the triangulation (#H by 2) and (`V2`

,`F2`

) is the
generated triangulation. Additional parameters can be passed to `triangle`

, to
control the quality: `"a0.005q"`

enforces a bound on the maximal area of the
triangles and a minimal angle of 20 degrees. In Example
604, the interior of a square (excluded a smaller square
in its interior) is triangulated.

Similarly, the interior of a closed manifold surface can be tetrahedralized
using the function `igl::tetrahedralize`

which wraps the Tetgen library (Example
605):

```
igl::tetrahedralize(V,F,"pq1.414", TV,TT,TF);
```

Ambient occlusion is a rendering technique used to calculate the exposure of each point in a surface to ambient lighting. It is usually encoded as a scalar (normalized between 0 and 1) associated with the vertice of a mesh.

Formally, ambient occlusion is defined as:

\[ A_p = \frac{1}{\pi} \int_\omega V_{p,\omega}(n \cdot \omega) d\omega \]

where \(V_{p,\omega}\) is the visibility function at p, defined to be zero if p is occluded in the direction \(\omega\) and one otherwise, and \(d\omega\) is the infinitesimal solid angle step of the integration variable \(\omega\).

The integral is usually approximated by casting rays in random directions around each vertex. This approximation can be computed using the function:

```
igl::ambient_occlusion(V,F,V_samples,N_samples,500,AO);
```

that given a scene described in `V`

and `F`

, computes the ambient occlusion of
the points in `V_samples`

whose associated normals are `N_samples`

. The
number of casted rays can be controlled (usually at least 300–500 rays are
required to get a smooth result) and the result is returned in `AO`

, as a
single scalar for each sample.

Ambient occlusion can be used to darken the surface colors, as shown in Example 606

Libigl supports read and writing to .png files via the stb image code.

With the viewer used in this tutorial, it is possible to render the scene in a
memory buffer using the function, `igl::viewer::ViewerCore::draw_buffer`

:

```
// Allocate temporary buffers for 1280x800 image
Eigen::Matrix<unsigned char,Eigen::Dynamic,Eigen::Dynamic> R(1280,800);
Eigen::Matrix<unsigned char,Eigen::Dynamic,Eigen::Dynamic> G(1280,800);
Eigen::Matrix<unsigned char,Eigen::Dynamic,Eigen::Dynamic> B(1280,800);
Eigen::Matrix<unsigned char,Eigen::Dynamic,Eigen::Dynamic> A(1280,800);
// Draw the scene in the buffers
viewer.core.draw_buffer(viewer.data,viewer.opengl,false,R,G,B,A);
// Save it to a PNG
igl::png::writePNG(R,G,B,A,"out.png");
```

In Example 607 a scene is rendered in a temporary png and used to texture a quadrilateral.

Extreme deformations or parametrizations with high-distortion might flip elements. This is undesirable in many applications, and it is possible to avoid it by introducing a non-linear constraints that guarantees that the area of every element remain positive.

Libigl can be used to compute Locally Injective Maps [34] using a variety of deformation energies. A simple deformation of a 2D grid is computed in Example 608.

Constructive solid geometry (CSG) is a technique to define a complex surface as
the result of a number of set operations on solid regions of space: union,
intersection, set difference, symmetric difference, complement. Typically, CSG
libraries represent the inputs and outputs to these operations *implicitly*:
the solid \(A\) is defined as the open set of points \(\mathbf{x}\) for which some
function \(a(\mathbf{x})\) “returns true”. The surface of this shape is the
*closure* of all points \(x\) in \(A\).

With this sort of representation, boolean operations are straightforward. For example, the union of solids \(A\) and \(B\) is simply

\(A \cup B = \{\mathbf{x} \left.\right| a(\mathbf{x}) \text{ or } b(\mathbf{x})\},\)

the intersection is

\(A \cap B = \{\mathbf{x} \left.\right| a(\mathbf{x}) \text{ and } b(\mathbf{x})\},\)

the difference \(A\) *minus* \(B\) is

\(A \setminus B = \{\mathbf{x} \left.\right| a(\mathbf{x}) \text{ and _not_ } b(\mathbf{x})\},\)

and the symmetric difference (XOR) is

\(A \triangle B = \{\mathbf{x} \left.\right| \text{either } a(\mathbf{x}) \text{ or } b(\mathbf{x}) \text{ but not both }\}.\)

Stringing together many of these operations, one can design quite complex
shapes. A typical CSG library might only keep explicit *base-case*
representations of canonical shapes: half-spaces, quadrics, etc.

In libigl, we do currently *not* have an implicit surface representation.
Instead we expect our users to be working with *explicit* triangle mesh
*boundary representations* of solid shapes. CSG operations are much hard to
compute robustly with boundary representations, but are nonetheless useful.

To compute a boolean operation on a triangle mesh with vertices `VA`

and
triangles `FA`

and another mesh `VB`

and `FB`

, libigl first computes a unified
“mesh arrangement” (see [35]) with vertices `V`

and triangles `F`

where all triangle-triangle
intersections have been “resolved”. That is, edges and vertices are added
exactly at the intersection lines, so the resulting *non-manifold* mesh `(V,F)`

has no self-intersections.

Then libigl labels each “cell” bounded by surfaces of the arrangement according
to its *winding number vector*: winding number with respect to each input mesh
\((w_A,w_B)\). Finally, according to the desired operation (e.g. union,
intersection) the boundary of the corresponding cells are extracted.

Calling libigl’s boolean operations is simple. To compute the union of
`(VA,FA)`

and `(VB,FB)`

into a new mesh `(VC,FC)`

, use:

```
igl::copyleft::cgal::mesh_boolean(VA,FA,VB,FB,MESH_BOOLEAN_TYPE_UNION,VC,FC);
```

The following figure shows each boolean operation on two meshes.

The union, symmetric difference and “resolve” have the same outward appearance, but differ in their treatment of internal structures. The union has no internal surfaces: the triangles are not included in the output. The symmetric difference is the same set of triangles as the “resolve”, but internal surfaces have been reversed in orientation, indicating that the solid result of the operation. The “resolve” operation is not really a boolean operation, it is simply the result of resolving all intersections and gluing together coincident vertices, maintaining original triangle orientations.

Libigl also provides a wrapper `igl::copyleft::cork::mesh_boolean`

to the
cork, which is typically faster, but is not
always robust.

The previous section discusses using
`igl::copyleft::cgal::mesh_boolean`

to compute the result of a *single* boolean
operation on two input triangle meshes. When employing constructive solid
geometry (CSG) as a modeling paradigm, shapes are represented as the result of
many such binary operations. The sequence is stored in a binary tree.

Libigl uses exact arithmetic internally to construct the intermediary boolean
results robustly. “Rounding” this result to floating point (even double
precision) would cause problems if re-injected into a further boolean
operation. To facilitate CSG tree operations and encourage callers *not* to
call `igl::copyleft::cgal::mesh_boolean`

multiple times explicitly, libigl implements
a class `igl::copyleft::cgal::CSGTree`

. Leaf nodes of this class are simply “solid”
meshes (otherwise good input to `igl::copyleft::cgal::mesh_boolean`

). Interior nodes
of the tree combine two children with a boolean operation. Using the intializer
list constructor it is easy to hard-code specific tree constructions. Here’s an
example taking the *intersection* of a cube A and sphere B *minus* the *union*
of three cylinders:

```
// Compute result of (A ∩ B) \ ((C ∪ D) ∪ E)
igl::copyleft::cgal::CSGTree<MatrixXi> CSGTree =
{{{VA,FA},{VB,FB},"i"},{{{VC,FC},{VD,FD},"u"},{VE,FE},"u"},"m"};
```

Example 610 computes each intermediary CSG result and then the final composite.

The Scalable Locally Injective Maps [36] algorithm allows to compute locally injective maps on massive datasets. The algorithm shares many similarities with ARAP, but uses a reweighting scheme to minimize arbitrary distortion energies, including those that prevent the introduction of flips.

Example 611 contains three demos: (1) an example of large scale 2D parametrization, (2) an example of 2D deformation with soft constraints, and (3) an example of 3D deformation with soft constraints. The implementation in libigl is self-contained and relies on Eigen for the solution of the linear system used in the global step. An optimized version that relies on Pardiso is available here.

Libigl contains a *wide* variety of geometry processing tools and functions for
dealing with meshes and the linear algebra related to them: far too many to
discuss in this introductory tutorial. We’ve pulled out a couple of the
interesting functions in this chapter to highlight.

Libigl contains various mesh statistics, including face angles, face areas and the detection of singular vertices, which are vertices with more or less than 6 neighbours in triangulations or 4 in quadrangulations.

The example Statistics computes these quantities and does a basic statistic analysis that allows to estimate the isometry and regularity of a mesh:

```
Irregular vertices:
136/2400 (5.67%)
Areas (Min/Max)/Avg_Area Sigma:
0.01/5.33 (0.87)
Angles in degrees (Min/Max) Sigma:
17.21/171.79 (15.36)
```

The first row contains the number and percentage of irregular vertices, which
is particularly important for quadrilateral meshes when they are used to define
subdivision surfaces: every singular point will result in a point of the
surface that is only C^{1}.

The second row reports the area of the minimal element, maximal element and the standard deviation. These numbers are normalized by the mean area, so in the example above 5.33 max area means that the biggest face is 5 times larger than the average face. An ideal isotropic mesh would have both min and max area close to 1.

The third row measures the face angles, which should be close to 60 degrees (90 for quads) in a perfectly regular triangulation. For FEM purposes, the closer the angles are to 60 degrees the more stable will the optimization be. In this case, it is clear that the mesh is of bad quality and it will probably result in artifacts if used for solving PDEs.

The problem of tetrahedralizing the interior of closed watertight surface mesh
is a difficult, but well-posed problem (see our Tetgen wrappers). But
black-box tet-meshers like TetGen will *refuse* input triangle meshes with
self-intersections, open boundaries, non-manifold edges from multiple connected
components.
The problem is two-fold: self-intersections present contradictory facet
constraints and self-intersections/open-boundaries/non-manifold edges make the
problem of determining inside from outside ill-posed without further
assumptions.

The first problem is *easily* solved by “resolving” all self-intersections.
That is, meshing intersecting triangles so that intersects occur exactly at
edges and vertices. This is accomplished using `igl::selfintersect`

.

TetGen can usually tetrahedralize the convex hull of this “resolved” mesh, and
then the problem becomes determining which of these tets are *inside* the input
mesh and which are outside. That is, which should be kept and which should be
removed.

The “Generalized Winding Number” is a robust method for determined
inside and outside for troublesome meshes [37]. The generalized
winding number with respect to `(V,F)`

at some point \(\mathbf{p} \in
\mathcal{R}^3\) is defined as scalar function:

\[w(\mathbf{p}) = \sum\limits_{f_i\in F} \frac{1}{4\pi}\Omega_{f_i}(\mathbf{p})\]

where \(\Omega_{f_i}\) is the *solid angle* subtended by \(f_i\) (the ith face in
`F`

) at the point \(\mathbf{p}\). This solid angle contribution is a simple,
closed-form expression involving `atan2`

and some dot-products.

If `(V,F)`

*does* form a closed watertight surface, then \(w(\mathbf{p})=1\) if
\(\mathbf{p}\) lies inside `(V,F)`

and \(w(\mathbf{p})=0\) if outside `(V,F)`

. If
`(V,F)`

is closed but overlaps itself then \(w(\mathbf{p})\) is an integer value
counting how many (signed) times `(V,F)`

*wraps* around \(\mathbf{p}\). Finally,
if `(V,F)`

is not closed or not even manifold (but at least consistently
oriented), then \(w(\mathbf{p})\) tends smoothly toward 1 as \(\mathbf{p}\) is
*more* inside `(V,F)`

, and toward 0 as \(\mathbf{p}\) is more outside.

The study of mesh simplification or *decimation* is nearly as old as meshes
themselves. Given a high resolution mesh with too many triangles, find a “well
approximating” low resolution mesh with far fewer triangles. By now there are a
variety of different paradigms for solving this problem and state-of-the-art
methods are fairly advanced.

One family of mesh decimation methods operates by successively remove elements from the mesh. In particular, Hoppe advocates for successively remove or rather collapsing edges [38]. The generic form of this technique is to construct a sequence of n meshes from the initial high-resolution mesh \(M_0\) to the lowest resolution mesh \(M_n\) by collapsing a single edge:

\(M_0 \mathop{\longrightarrow}_\text{edge collapse} M_1 \mathop{\longrightarrow}_\text{edge collapse} \dots \mathop{\longrightarrow}_\text{edge collapse} M_{n-1} \mathop{\longrightarrow}_\text{edge collapse} M_n.\)

Hoppe’s original method and subsequent follow-up works propose various ways to choose the next edge to collapse in this sequence. Using a cost-based paradigm, one can maintain a priority queue of edges based on their “cost” (how much “worse” will my approximation be if I remove this edge?). The cheapest edge is collapsed and costs of neighboring edges are updated.

In order to maintain the topology (e.g. if the mesh is combinatorially as sphere or a torus etc.), one should assign infinite cost to edges whose collapse would alter the mesh topology. Indeed this happens if and only if the number of mutual neighbors of the endpoints of the collapsing edge is not exactly two!

If there exists a third shared vertex, then another face will be removed, but 2 edges will be removed. This can result in unwanted holes or non-manifold “flaps”.

There is also a one-off condition that no edges of a tetrahedron should be collapsed.

Because libigl (purposefully) does not center its implementations around a dynamic mesh data structure (e.g. half-edge datastructure), support for topology changes are limited. Nonetheless, libigl has support for isolated edge collapses, sequences of edge-collapses (each in O(log) time) and priority queue based decimation.

The simplest is `igl::decimation`

. By calling

```
igl::decimate(V,F,1000,U,G);
```

the mesh `(V,F)`

will be decimated to a new mesh `(U,G)`

so that `G`

has at
most `1000`

faces. This uses default (naive) criteria for determining the cost
of an edge collapse and the placement of the merged vertex. Shortest edges are
collapsed first, and merged vertices are placed at edge midpoints.

One can also provide function handles (`c++`

lambda functions are convenient
here) `cost_and_placement`

and `stopping_condition`

for determining the
cost/placement of an edge collapse and the stopping condition respectively. For
example, the default version above is implemented as:

```
igl::decimate(V,F,shortest_edge_and_midpoint,max_m,U,G);
```

where `shortest_edge_and_midpoint`

assign the edge’s length as cost and its
midpoint as the merged vertex placement and `max_m`

counts the current number
of faces (valid collapses decrease count by 2) and returns `true`

if the count
drops below `m=1000`

.

One can also scratch deeper inside the decimation loop and call
`igl::collapse_edge`

directly. In order to operate efficiently, this routine
needs more than the usual `(V,F)`

mesh representation. We need `E`

a list of
edge indices, where `E.row(i) --> [s,d]`

; we need `EMAP`

which maps the
“half”-edges of each triangle in `F`

to its corresponding edge in `E`

so that
`E.row(EMAP(f+i*F.rows)) --> [s,d]`

if the edge across from the ith corner of the
fth face is `[s,d]`

(up to orientation); we need `EF`

and `EI`

which keep track
of the faces incident on each edge and across from which corner of those faces
the edges appears, so that `EF(e,o) = f`

and `EI(e,o) = i`

means that the edge
`E.row(e) --> [s,d]`

appears in the fth face across from its ith corner (for
`o=0`

the edge orientations should match, for `o=1`

the orientations are
opposite).

When a collapse occurs, the sizes of the `F`

,`E`

, etc. matrices do not change.
Rather rows corresponding to “removed” faces and edges are set to a special
constant value `IGL_COLLAPSE_EDGE_NULL`

. Doing this ensures that we’re able to
remove edges in truly constant time O(1).

Conveniently

`IGL_COLLAPSE_EDGE_NULL==0`

. This means most OPENGL style renderings of`F`

will simply draw a bunch of 0-area triangles at the first vertex.

The following will collapse the first edge and place its merged vertex at the origin:

```
igl::collapse_edge(0,RowVector3d(0,0,0),V,F,E,EMAP,EF,EI);
```

If valid, then `V`

,`F`

,`E`

,`EF`

,`EI`

are adjusted accordingly.

This is powerful, but low level. To build a decimator around this you’d need to keep track which edges are left to collapse and which to collapse next. Fortunately, libigl also exposes a priority queue based edge collapse with function handles to adjust costs and placements.

The priority queue is implemented as a (ordered) set `Q`

or (cost,edge index)
pairs and a list of iterators `Qit`

so that `Qit[e]`

reveals the iterator in
`Q`

corresponding to the eth edge. Placements are stored in a #E list of
positions `C`

. When the following is called:

```
igl::collapse_edge(cost_and_placement,V,F,E,EMAP,EF,EI,Q,Qit,C);
```

the lowest cost edge collapse according to `Q`

is attempted. If valid, then
`V`

,`F`

,etc. are adjusted accordingly and that edge is “popped” from `Q`

. Using
`Qit`

its neighboring edges are also popped from `Q`

and re-inserted after
updating their costs according to `cost_and_placement`

, new placements are
remembered in `C`

. If not valid, then the edge is “popped” from `Q`

and
reinserted with infinite cost.

The Example 703 demonstrates using this priority queue based approach with the simple shortest-edge-midpoint cost/placement strategy discussed above.

In the Generalized Winding Number section, we
examined a robust method for determining whether points lie inside or outside
of a given triangle soup mesh. Libigl complements this algorithm with
accelerated signed and unsigned distance queries and “in element” queries for
planar triangle meshes and 3D tetrahedral meshes. These routines make use of
libigl’s general purpose axis-aligned bounding box hierarchy (`igl/AABB.h`

).
This class is lightweight and—by design—does not store a copy of the mesh
(taking it as inputs to its member functions instead).

For tetrahedral meshes, this is useful for “in element” or “point location”
queries: given a point \(\mathbf{q}\in\mathcal{R}^3\) and a tetrahedral mesh
\((V,T)\) determine in which tetrahedron \(\mathbf{q}\) lies. This is accomplished
in libigl for a tet mesh `V,T`

and a list of query points in the rows of `Q`

via the `igl::in_element()`

:

```
// Initialize AABB tree
igl::AABB<MatrixXd,3> tree;
tree.init(V,T);
VectorXi I;
igl::in_element(V,T,Q,tree,I);
```

the resulting vector `I`

is a list of indices into `T`

revealing the *first*
tetrahedron found to contain the corresponding point in `Q`

.

For overlapping meshes, a point \(\mathbf{q}\) may belong to more than one
tetrahedron. In those cases, one can find them all (not just the first) by
using the `igl::in_element`

overload with a `SparseMatrix`

as the output:

```
SparseMatrix<int> I;
igl::in_element(V,T,Q,tree,I);
```

now each row of `I`

reveals whether each tet contains the corresponding row in
`Q`

: `I(q,e)!=0`

means that point `q`

is in element `e`

.

For Triangle meshes, we use the AABB tree to accelerate point-mesh closest
point queries: given a mesh \((V,F)\) and a query point
\(\mathbf{q}\in\mathcal{R}^3\) find the closest point \(\mathbf{c} \in (V,F)\)
(where \(\mathbf{c}\) is not necessarily a vertex of \((V,F)\)). This is
accomplished for a triangle mesh `V,F`

and a list of points in the rows of `P`

via `igl::point_mesh_squared_distance`

:

```
VectorXd sqrD;
VectorXi I;
MatrixXd C;
igl::point_mesh_squared_distance(P,V,F,sqrD,I,C);
```

the output `sqrD`

contains the (unsigned) squared distance from each point in
`P`

to its closest point given in `C`

which lies on the element in `F`

given by
`I`

(e.g. from which one could recover barycentric coordinates, using
`igl::barycentric_coordinates`

).

If the mesh `V,F`

is static, but the point set `P`

is changing dynamically then
it’s best to reuse the AABB hierarchy that’s being built during
`igl::point_mesh_squared_distance`

:

```
igl::AABB tree;
tree.init(V,F);
tree.squared_distance(V,F,P,sqrD,I,C);
... // P changes, but (V,F) does not
tree.squared_distance(V,F,P,sqrD,I,C);
```

Finally, from the closest point or the winding number it’s possible to *sign*
this distance. In `igl::signed_distance`

we provide two methods for signing:
the so-called “pseudo-normal test” [39] and the generalized
winding number [37].

The pseudo-normal test (see also `igl::pseudonormal_test`

) assumes the input
mesh is a watertight (closed, non-self-intersecting, manifold) mesh. Then given
a query point \(\mathbf{q}\) and its closest point \(\mathbf{c} \in (V,F)\), it
carefully chooses an outward normal \(\mathbf{n}\) at \(\mathbf{c}\) so that
\(\text{sign}(\mathbf{q}-\mathbf{c})\cdot \mathbf{n}\) reveals whether
\(\mathbf{q}\) is inside \((V,F)\): –1, or outside: +1. This is a fast \(O(1)\) test
once \(\mathbf{c}\) is located, but may fail if `V,F`

is not watertight.

An alternative is to use the generalized winding
number to determine the sign. This is very robust to
unclean meshes `V,F`

but slower: something like \(O(\sqrt{n})\) once \(\mathbf{c}\)
is located.

In either case, the interface via `igl::signed_distance`

is:

```
// Choose type of signing to use
igl::SignedDistanceType type = SIGNED_DISTANCE_TYPE_PSEUDONORMAL;
igl::signed_distance(P,V,F,sign_type,S,I,C,N);
```

the outputs are as above for `igl::point_mesh_squared_distance`

but now `S`

contains signed (unsquared) distances and the extra output `N`

(only set when
`type == SIGNED_DISTANCE_TYPE_PSEUDON`

) contains the normals used for signing
with the pseudo-normal test.

Often 3D data is captured as scalar field defined over space \(f(\mathbf{x}) :
\mathcal{R}^3 \rightarrow \mathcal{R}\). Lurking within this field,
*iso-surfaces* of the scalar field are often salient geometric objects. The
iso-surface at value \(v\) is composed of all points \(\mathbf{x}\) in
\(\mathcal{R}^3\) such that \(f(\mathbf{x}) = v\). A core problem in geometry
processing is to extract an iso-surface as a triangle mesh for further
mesh-based processing or visualization. This is referred to as iso-contouring.

“Marching Cubes” [40] is a famous method for iso-contouring tri-linear functions \(f\) on a regular lattice (aka grid). The core idea of this method is to contour the iso-surface passing through each cell (if it does at all) with a predefined topology (aka connectivity) chosen from a look up table depending on the function values at each vertex of the cell. The method iterates (“marches”) over all cells (“cubes”) in the grid and stitches together the final, watertight mesh.

In libigl, `igl::marching_cubes`

constructs a triangle mesh `(V,F)`

from an
input scalar field `S`

sampled at vertex locations `GV`

of a `nx`

by `ny`

by
`nz`

regular grid:

```
igl::marching_cubes(S,GV,nx,ny,nz,V,F);
```

Models from the web occasionally arrive *unorientated* in the sense that
the orderings of each triangles vertices do not consistently agree. Determining
a consistent facet orientation for a mesh is essential for two-sided lighting
(e.g., a cloth with red velvet on one side and gold silk on the other side) and
for inside-outside determination(e.g., using generalized winding
numbers).

For (open) surfaces representing two-sided sheets, libigl provides a routine to
force consistent orientations within each orientable patch
(`igl::orientable_patches`

) of a mesh:

```
igl::bfs_orient(F,FF,C);
```

This simple routine will use breadth-first search on each patch of the mesh to
enforce a consistent facet orientation in the output faces `FF`

.

For (closed or nearly closed) surfaces representing the boundary of a solid
object, libigl provides a routine to reorient faces so that the vertex ordering
corresponds to a counter-clockwise ordering of the vertices with a
right-hand-rule normal pointing outward. This method [41] assumes
that most of the universe is
empty.
That is, most points in space are outside of the solid object than inside.
Points are sampled over surface patches. For each sample point, rays are shot
into both hemispheres to compute average of the (distance weighted) ambient
occlusion on each side. A patch is oriented so that the outward side is *less
occluded* (lighter, i.e., facing more void space).

```
igl::embree::reorient_facets_raycast(V,F,FF,I);
```

The boolean vector `I`

reveals which rows of `F`

have been flipped in `FF`

.

The swept volume \(S\) of a moving solid object \(A\) can be defined as any point in space such that at one moment in time the point lies inside the solid. In other words, it is the union of the solid object transformed by the rigid motion \(f(t)\) over time:

\(S = \bigcup \limits_{t\in [0,1]} f(t) A.\)

The surface of the swept volume of a solid bounded by a triangle mesh
undergoing a rigid motion with non-trivial rotation is * not* a surface
exactly representably by triangle mesh: it will be a piecewise-ruled surface.

To see this, consider the surface swept by a single edge’s line segment as it performs a screw motion.

This means that if we’d like to the surface of the swept volume of a triangle mesh undergoing a rigid motion and we’d like the output to be another triangle mesh, then we’re going to have to be happy with some amount of approximation error.

With this in mind, the simplest method for computing an approximate swept volume is by exploiting an alternative definition of the swept volume based on signed distances:

\(S = \left\{ \mathbf{p}\ \middle| \ d(\mathbf{p},\partial S) < 0 \right\} = \left\{ \mathbf{p}\ \middle|\ \min\limits_{t \in [0,1]} d(\mathbf{p},f(t)\ \partial A) < 0 \right\}\)

If \(\partial A\) is a triangle mesh, then we can approximate this by 1) discretizing time at a finite step of steps \([0,\Delta t,2\Delta t, \dots, 1]\) and by 2) discretizing space with a regular grid and representing the distance field using trilinear interpolation of grid values. Finally the output mesh, \(\partial S\) is approximated by contouring using Marching Cubes [40].

This method is similar to one described by Schroeder et al. in 1994 [42], and the one used in conjunction with boolean operations by Garg et al. 2016 [43].

In libigl, if your input solid’s surface is represented by `(V,F)`

then the
output surface mesh will be `(SV,SF)`

after calling:

```
igl::copyleft::swept_volume(V,F,num_time_steps,grid_size,isolevel,SV,SF);
```

The `isolevel`

parameter can be set to zero to approximate the exact swept
volume, greater than zero to approximate a positive offset of the swept volume
or less than zero to approximate a negative offset.

Picking vertices and faces using the mouse is very common in geometry processing applications. While this might seem a simple operation, its implementation is not straighforward. Libigl contains a function that solves this problem using the Embree raycaster. Its usage is demonstrated in Example 708:

```
bool hit = igl::unproject_onto_mesh(
Vector2f(x,y),
F,
viewer.core.view * viewer.core.model,
viewer.core.proj,
viewer.core.viewport,
*ei,
fid,
vid);
```

This function casts a ray from the view plane in the view direction. Variables
`x`

and `y`

are
the mouse screen coordinates; `view`

, `model`

, `proj`

are the view, model and
projection matrix respectively; `viewport`

is the viewport in OpenGL format;
`ei`

contains a Bounding Volume
Hierarchy constructed
by Embree, and `fid`

and `vid`

are the picked face and vertex, respectively.

Vector fields on surfaces are commonly visualized by tracing streamlines. Libigl
supports the seeding and tracing of streamlines, for both simple vector fields
and for N-rosy fields. The seeds for the streamlines are initialized using `streamlines_init`

,
and the lines are traced using `streamlines_next`

. Each call to `streamlines_next`

extends
each line by one triangle, allowing interactive rendering of the traced lines, as demonstrated
in Example 709.

Libigl is in active development, and we plan to focus on the following features in the next months:

A better and more consistent

**documentation**, plus extending this tutorial to cover more libigl features.Implement a

**mixed-integer solver**which only uses Eigen to remove the dependency on CoMiSo.Improve the robustness and performance of the active set QP solver. In particular, handle linearly dependent constraints.

Implement more mesh analysis functions, including structural analysis for masonry and

*3D-printability*analysis.Increase support for point clouds and general polygonal meshes.

What would you like to see in libigl? Contact us! or post a feature request.

We encourage you to contribute to the library and to report problems and bugs. The best way to contribute new feature or bug fixes is to fork the libigl repository and to open a pull request on our github repository.

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