Chapter 56
Estimation of Local Differential Properties

Marc Pouget and Frédéric Cazals

Table of Contents

56.1 Introduction
   56.1.1   Overview
   56.1.2   Smooth Surfaces, d-Jets and the Monge Form
   56.1.3   Algorithm
   56.1.4   Degenerate Cases
56.2 Software Design
   56.2.1   Options and Interface Specifications
   56.2.2   Output
   56.2.3   Template Parameters
56.3 Examples
   56.3.1   Single Estimation about a Point of a Point Cloud
   56.3.2   On a Mesh
56.4 Mathematical and Algorithmic Details
   56.4.1   Computing a Basis for the Fitting
   56.4.2   Solving the Interpolation / Approximation Problem
   56.4.3   Principal Curvature / Directions
   56.4.4   Computing Higher Order Monge Coefficients

Figure 56.1:  Principal directions associated with kmax scaled by kmin.

This package allows the estimation of local differential quantities of a surface from a point sample, given either as a mesh or as point cloud.

Note that this package needs the third party libraries Lapack and Blas to be installed to compile the example code.

56.1   Introduction

56.1.1   Overview

Consider a sampled smooth surface, and assume we are given a collection of points P about a given sample p. We aim at estimating the differential properties up to any fixed order of the surface at point p from the point set P+ = P { p} - we denote N= | P+ | . More precisely, first order properties correspond to the normal or the tangent plane; second order properties provide the principal curvatures and directions, third order properties provide the directional derivatives of the principal curvatures along the curvature lines, etc. Most of the time, estimating first and second order differential quantities is sufficient. However, some applications involving shape analysis require estimating third and fourth order differential quantities. Many different estimators have been proposed in the vast literature of applied geometry [Pet01] (section 3, page 7), and all of them need to define a neighborhood around the point at which the estimation is computed. Our method relies on smooth differential geometry calculations, carried out on smooth objects fitted from the sample points. Datasets amenable to such a processing are naturally unstructured point clouds, as well as meshes - whose topological information may be discarded.

Estimating differential properties from discrete date always raises a philosophical issue. On one hand, estimating differential quantities subsumes a smooth surface does exist. In this spirit one wishes to recover its differential properties, so that any estimation method must come with an asymptotic convergence analysis of the results returned. For the method developed in this Cgal package, the interested will find such an analysis in [CP05a], (Theorem 3) - it should be stressed the error bounds proved therein are optimal.

On the other hand, any estimation method may be applied to arbitrarily data - surface unknown, surface piecewise smooth etc. In such a case, no analysis can be carried out, and it is up to the users to check the results match their needs.

Unlike most of the Cgal packages, this package uses approximation methods and is not intended to provide an exact canonical result in any sense. This is why internal computations are performed with a number type possibly different from that of the input data, even if for convenience the results are returned with this original number type. A reasonable choice for this internal number type is for example the double type.

56.1.2   Smooth Surfaces, d-Jets and the Monge Form

To present the method, we shall need the following notions. Consider a smooth surface. About one of its points, consider a coordinate system whose z-axis does not belong to the tangent space. In such a frame, the surface can locally be written as the graph of a bivariate function. Letting h.o.t. stand for higher order terms, one has :

z(x,y)=JB,d(x,y) + h.o.t. ;   JB,d(x,y)=

The degree d polynomial JB,d is the Taylor expansion of the function z, and is called its d-jet. Notice that a d-jet contains Nd=(d+1)(d+2)/2 coefficients.

Recall that an umbilical point of a surface - or umbilic for short, is a point where both principal curvatures are identical. At any point of the surface which is not an umbilic, principal directions d1, d2 are well defined, and these (non oriented) directions together with the normal vector n define two direct orthonormal frames. If v1 is a unit vector of direction d1, there exists a unique unit vector v2 so that (v1,v2,n) is direct; and the other possible frame is (-v1,-v2,n). Both these coordinate systems are known as the Monge coordinate systems. In both these systems, the surface is said to be given in the Monge form and its jet has the following canonical form :

z(x,y) =

(k1x2 + k2y2)+


(c0x4+4c1x3y+6c2x2y2+4c3xy3+c4y4) + h.o.t.

The coefficients k1, k2 are the principal curvatures, b0,b3 are the directional derivatives of k1,k2 along their respective curvature line, while b1,b2 are the directional derivatives of k1,k2 along the other curvature lines.

The Monge coordinate system can be computed from any d-jet (d 2), and so are the Monge coefficients. These informations characterize the local geometry of the surface in a canonical way, and are the quantities returned by our algorithm.

56.1.3   Algorithm

Based on the above concepts, the algorithm consists of 4 steps.
  1. We perform a Principal Component Analysis (PCA) on P+. This analysis outputs three orthonormal eigenvectors and the associated eigenvalues. The fitting basis consists of these three vectors so that the vector associated to the smallest eigenvalue is the last vector of the basis. (Indeed, if the surface is well sampled, one expects the PCA to provide one small and two large eigenvalues, the eigenvector associated to the small one approximating the normal vector.)
  2. We perform a change of coordinates to move the samples into the coordinate system of the fitting basis and with origin the point p at which the estimation is sought. We then resort to polynomial fitting, so as to either interpolate or approximate the d-jet of the surface in this coordinate system. This bivariate polynomial approximation reduces to linear algebra operations.
  3. From the fitted d-jet, we compute the Monge basis (d1,d2,n).
  4. Finally, we compute the Monge coefficients : ki, bi, ci.

Further details can be found in section 56.4 and in [CP05a] (section 6).

56.1.4   Degenerate Cases

As usual, the fitting procedure may run into (almost) degenerate cases:

In these cases, even if a result is provided, the estimation may not be relevant. To inform the user of these issues, we provide the PCA results and the condition number of the fitting. In any case, it is up to the user to judge if the result meets its need.

56.2   Software Design

56.2.1   Options and Interface Specifications

The fitting strategy performed by the class Monge_via_jet_fitting requires the following parameters:

56.2.2   Output

As explained in Section 56.1, the output consists of a coordinate system, the Monge basis, together with the Monge coefficients which are stored in the Monge_form class. In addition, more information on the computational issues are stored in the Monge_via_jet_fitting class.

The Monge_form class provides the following information.

In addition, the class Monge_via_jet_fitting stores

56.2.3   Template Parameters

Template parameter DataKernel

This concept provides the types for the input sample points, together with 3d vectors and a number type. It is used as template for the classe Monge_via_jet_fitting<DataKernel, LocalKernel = Cartesian<double>, SvdTraits = lapack_svd> . Typically, one can use CGAL::Cartesian<double>.

Template parameter LocalKernel

This is a parameter of the class Monge_via_jet_fitting<DataKernel, LocalKernel = Cartesian<double>, SvdTraits = lapack_svd>. This concept defines the vector and number types used for local computations and to store the PCA basis data.

Input points of type DataKernel::Point_3 are converted to LocalKernel::Point_3. For output of the Monge_form class, these types are converted back to Data_Kernel ones. Typically, one can use CGAL::Cartesian<double> which is the default.

Template parameter SvdTraits

This concept provides the number, vector and matrix types for algebra operations required by the fitting method in Monge_via_jet_fitting<DataKernel, LocalKernel = Cartesian<double>, SvdTraits = lapack_svd> . The main method is a linear solver using a singular value decomposition.

Compatibility requirements

To solve the fitting problem, the sample points are first converted from the DataKernel to the LocalKernel (this is done using the CGAL::Cartesian_converter). Then change of coordinate systems and linear algebra operations are performed with this kernel. This implies that the number types LocalKernel::FT and SvdTraits::FT must be identical. Second the Monge basis and coefficients, computed with the LocalKernel, are converted back to the DataKernel (this is done using the CGAL::Cartesian_converter and the CGAL::NT_converter).

56.3   Examples

56.3.1   Single Estimation about a Point of a Point Cloud

The first example illustrates the computation of the local differential quantities from a set of points given in a text file as input. The first point of the list is the one at which the computation is performed. The user has to specify a file for the input points and the degrees d and d'.

File: examples/Jet_fitting_3/Single_estimation.cpp
#include <CGAL/Cartesian.h>

#include <fstream>
#include <vector>

#include <CGAL/Monge_via_jet_fitting.h>
typedef double                   DFT;
typedef CGAL::Cartesian<DFT>     Data_Kernel;
typedef Data_Kernel::Point_3     DPoint;
typedef CGAL::Monge_via_jet_fitting<Data_Kernel> My_Monge_via_jet_fitting;
typedef My_Monge_via_jet_fitting::Monge_form     My_Monge_form;

int main(int argc, char *argv[])
  size_t d_fitting = 4;
  size_t d_monge = 4;
  const char* name_file_in = "data/in_points_d4.txt";
  //check command line
  if (argc<4)
      std::cout << " Usage : " << argv[0]
                << " <inputPoints.txt> <d_fitting> <d_monge>" << std::endl
                << "test with default arguments" << std::endl;
  else {
    name_file_in = argv[1];
    d_fitting = std::atoi(argv[2]);
    d_monge = std::atoi(argv[3]);

  //open the input file
  std::ifstream inFile(name_file_in);
  if ( !inFile )
      std::cerr << "cannot open file for input\n";
  //initalize the in_points container
  double x, y, z;
  std::vector<DPoint> in_points;
  while (inFile >> x) {
    inFile >> y >> z;
    DPoint p(x,y,z);
  // fct parameters

  My_Monge_form monge_form;
  My_Monge_via_jet_fitting monge_fit;
  monge_form = monge_fit(in_points.begin(), in_points.end(), d_fitting, d_monge);

  //OUTPUT on std::cout
  std::cout << "vertex : " << in_points[0] << std::endl
            << "number of points used : " << in_points.size() << std::endl
            << monge_form;
  std::cout  << "condition_number : " << monge_fit.condition_number() << std::endl
             << "pca_eigen_vals and associated pca_eigen_vecs :"  << std::endl;
  for (int i=0; i<3; i++)
    std::cout << monge_fit.pca_basis(i).first << std::endl
              << monge_fit.pca_basis(i).second  << std::endl;
  return 0;

56.3.2   On a Mesh

The second example (cf Mesh_estimation.cpp in the example directory) illustrates the computation of local differential quantities for all vertices of a given mesh. The neighborhood of a given vertex is computed using rings on the triangulation. Results are twofold:

Figs. 56.1 and 56.2 provide illustrations of principal directions of curvature.

Figure 56.2:  Principal directions of curvature and normals at vertices of a mesh of the graph of the function f(x,y)=2x2+y2.

56.4   Mathematical and Algorithmic Details

In this Section, we detail the mathematics involved, in order to justify the design choices made. To begin with, observe the fitting problem involves three relevant direct orthonormal basis: the world-basis (wx,wy,wz), the fitting-basis (fx,fy,fz), the Monge basis (d1,d2,n).

Figure 56.3:  The three bases involved in the estimation.

56.4.1   Computing a Basis for the Fitting

Input : samples
Output : fitting-basis

Performing a PCA requires diagonalizing a symmetric matrix. This analysis gives an orthonormal basis whose z-axis is provided by the eigenvector associated to the smallest eigenvalue.1 Note one may have to swap the orientation of a vector to get a direct basis.

Let us denote PW F the matrix that changes coordinates from the world-basis (wx,wy,wz) to the fitting-basis (fx,fy,fz). The rows of PW F are the coordinates of the vectors (fx,fy,fz) in the world-basis. This matrix represents a orthogonal transformation hence its inverse is its transpose. To obtain the coordinates of a point in the fitting-basis from the coordinates in the world-basis, one has to multiply by PW F.

As mentioned above, the eigenvalues are returned, from which the sampling quality can be assessed. For a good sampling, the eigenvector associated to the smallest eigenvalue should roughly give the normal direction.

56.4.2   Solving the Interpolation / Approximation Problem

Input : samples, fitting-basis
Output : coefficients Ai,j of the bivariate fitted polynomial in the fitting-basis

Computations are done in the fitting-basis and the origin is the point p. First, one has to transform coordinates of sample points with a translation (-p) and multiplication by PW F.

The fitting process consists of finding the coefficients Ai,j of the degree d polynomial



Denote pi=(xi,yi,zi), i=1, , N the coordinates of the sample points of P+. For interpolation the linear equations to solve are A(xi,yi)=zi i=1, ,N, and for approximation one has to minimize i=1N (A(xi,yi)-zi)2. The linear algebra formulation of the problem is given by

A = (A0,0, A1,0,A0,1, , A0,d)T
Z= (z1, z2, , zN)T
M= (1,xi, yi,

, ,


The equations for interpolation become MA=Z. For approximation, the system MA=Z is solved in the least square sense, i.e. one seeks the vector A such that A = arg min A ||MA-Z||2.

In any case, there is a preconditioning of the matrix M so as to improve the condition number. Assuming the {xi}, {yi} are of order h, the pre-conditioning consists of performing a column scaling by dividing each monomial xikyil by hk+l - refer to Eq. (56.4.2). Practically, the parameter h is chosen as the mean value of the {xi} and {yi}. In other words, the new system is M'Y=(MD-1)(DA)=Z with D the diagonal matrix D=(1,h,h,h2, ,hd,hd), so that the solution A of the original system is A=D-1Y.

There is always a single solution since for under constrained systems we also minimize ||A||2. The method uses a singular value decomposition of the N × Nd matrix M= U S VT, where U is a N × N orthogonal matrix, V is a Nd × Nd orthogonal matrix and S is a N × Nd matrix with the singular values on its diagonal. Denote r the rank of M, we can decompose S= (

Dr 0r, Nd-r
0N-r, r 0N-r, Nd-r
). The number r, which is the number of non zero singular values, is strictly lower than Nd if the system is under constrained. In any case, the unique solution which minimize ||A||2 is given by :
A= V (
Dr-1 0Nd-r, r
0r, N-r 0Nd-r, N-r
) UTZ.
One can provide the condition number of the matrix M (after preconditioning) which is the ratio of the maximal and the minimal singular values. It is infinite if the system is under constrained, that is the smallest singular value is zero.

Implementation details. We assume a solve function is provided by the traits SvdTraits. This function solves the system MX=B (in the least square sense if M is not square) using a Singular Value Decomposition and gives the condition number of M.

Remark: as an alternative, other methods may be used to solve the system. A QR decomposition can be substituted to the SVD. One can also use the normal equation MTMX=MTB and apply methods for square systems such as LU, QR or Cholesky since MTM is symmetric definite positive when M has full rank. The advantages of the SVD is that it works directly on the rectangular system and gives the condition number of the system. For more on these alternatives, see [GvL83] (Chap. 5).

56.4.3   Principal Curvature / Directions

Input : coefficients of the fit Ai,j, fitting-basis
Output : Monge basis wrt fitting-basis and world-basis

In the fitting basis, we have determined a height function expressed by Eq. (56.4.2). Computations are done in the fitting-basis. The partial derivatives, evaluated at (x,y)=(0,0), of the fitted polynomial JA,d(x,y) are Ai,j=(i+jJA,d)/(ix ∂jy) Expanding Eq. (56.4.2) yields:

JA,d(x,y) = A0,0+A1,0x+A0,1y+

(A2,0x2+2A1,1xy+A0,2y2) +

(A3,0x3+3A2,1x2y+ )+

56.4.4   Computing Higher Order Monge Coefficients

Input : coefficients of the fit, Monge basis wrt fitting-basis (PF M)
Output : third and fourth order coefficients of Monge

We use explicit formula. The implicit equation of the fitted polynomial surface in the fitting-basis with origin the point (0,0,A0,0) is Q=0 with

Q=-w-A0,0 +

The equation in the Monge basis is obtained by substituting (u,v,w) by PTF M(x,y,z). Denote f(x,y,z)=0 this implicit equation. By definition of the Monge basis, we have locally (at (0,0,0))
f(x,y,z)=0 z=g(x,y)
and the Taylor expansion of g at (0,0) are the Monge coefficients sought. Let us denote the partial derivatives evaluated at the origin of f and g by fi,j,k=(i+j+kf)/(ix ∂jy ∂kz) and gi,j=(i+jg)/(ix ∂jy). One has f1,0,0=f0,1,0=f1,1,0=0, g0,0=g1,0=g0,1=g1,1=0 and g2,0=k1, g0,2=k2. The partial derivative of order n of f depends on the matrix PF M and the partial derivatives of order at most n of JA,d. The third and fourth order coefficients of are computed with the implicit function theorem. For instance :
f3,0,0 f0,0,1 -3 f1,0,1 f2,0,0

f0,0,1 2
- f0,1,1 f2,0,0 + f2,1,0 f0,0,1

f0,0,1 2


 1  Another possibility is to choose as z-axis the axis of the world-basis with the least angle with the axis determined with the PCA. Then the change of basis reduces to a permutation of axis.