This chapter describes Cgal's interpolation package which implements natural neighbor coordinate functions as well as different methods for scattered data interpolation most of which are based on natural neighbor coordinates. The functions for computing natural neighbor coordinates in Euclidean space are described in Section 69.1, the functions concerning the coordinate and neighbor computation on surfaces are discussed in Section 69.2. In Section 69.3, we describe the different interpolation functions.
Scattered data interpolation solves the following problem: given measures of a function on a set of discrete data points, the task is to interpolate this function on an arbitrary query point. More formally, let P={p1, … ,pn} be a set of n points in ℝ2 or ℝ3 and Φ be a scalar function defined on the convex hull of P. We assume that the function values are known at the points of P, i.e. to each pi ∈ P, we associate zi = Φ(pi). Sometimes, the gradient of Φ is also known at pi. It is denoted gi= ∇Φ(pi). The interpolation is carried out for an arbitrary query point x on the convex hull of P.
69.1 Natural Neighbor Coordinates
69.1.1 Introduction
Natural neighbor interpolation has been introduced by Sibson [Sib81] to interpolate multivariate scattered data. Given a set of data points P, the natural neighbor coordinates associated to P are defined from the Voronoi diagram of P. When simulating the insertion of a query point x into the Voronoi diagram of P, the potential Voronoi cell of x ``steals'' some parts from the existing cells.
Figure 69.1: 2D example: x has five natural neighbors p1, … , p5. The natural neighbor coordinate λ3(x) is the ratio of the area of the pink polygon, π3(x), over the area of the total highlighted zone.
Let π(x) denote the volume of the potential Voronoi cell of x and πi(x) denote the volume of the sub-cell that would be stolen from the cell of pi by the cell of x. The natural neighbor coordinate of x with respect to the data point pi ∈ P is defined by
| λi(x) = |
| . |
Various papers ([Sib80], [Far90], [Pip93], [Bro97], [HS00]) show that the natural neighbor coordinates have the following properties:
- x = ∑i=1n λi(x) pi (barycentric coordinate property).
- For any i,j ≤ n, λi(pj)= δij, where δij is the Kronecker symbol.
- ∑i=1n λi(x) = 1 (partition of unity property).
π(x) = ∞
λi(x) = 0 for all data point pi of P except for the two endpoints, let's say p and q ,of the edge where x lies.
The natural neighbor coordinate of x with respect to these endpoints p and q will be :
λp(x) = (||x - q|| )/( ||q - p||)
λq(x) = (||x - p|| )/( ||q - p||)
Furthermore, Piper [Pip93] shows that the coordinate
functions are continuous in the convex hull of P and
continuously differentiable except on the data points P.
The interpolation package of Cgal provides functions to compute natural neighbor coordinates for 2D and 3D points with respect to Voronoi diagrams as well as with respect to power diagrams (only 2D), i.e. for weighted points. Refer to the reference pages natural_neighbor_coordinates_2, natural_neighbor_coordinates_3 and regular_neighbor_coordinates_2.
In addition, the package provides functions to compute natural neighbor coordinates on well sampled point set surfaces. See Section 69.2 and the reference page surface_neighbor_coordinates_3 for further information.
69.1.2 Implementation
Given a Delaunay triangulation or a Regular triangulation, the vertices in conflict with the query point are determined. The areas πi(x) are computed by triangulating the Voronoi sub-cells. The normalization factor π(x) is also returned. If the query point is already located and/or the boundary edges of the conflict zone are already determined, alternative functions allow to avoid the re-computation.69.1.3 Example for Natural Neighbor Coordinates
The signature of all coordinate computation functions is about the same.
File: examples/Interpolation/nn_coordinates_2.cpp
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/Delaunay_triangulation_2.h>
#include <CGAL/natural_neighbor_coordinates_2.h>
typedef CGAL::Exact_predicates_inexact_constructions_kernel K;
typedef CGAL::Delaunay_triangulation_2<K> Delaunay_triangulation;
typedef std::vector< std::pair< K::Point_2, K::FT > >
Point_coordinate_vector;
int main()
{
Delaunay_triangulation dt;
for (int y=0 ; y<3 ; y++)
for (int x=0 ; x<3 ; x++)
dt.insert(K::Point_2(x,y));
//coordinate computation
K::Point_2 p(1.2, 0.7);
Point_coordinate_vector coords;
CGAL::Triple<
std::back_insert_iterator<Point_coordinate_vector>,
K::FT, bool> result =
CGAL::natural_neighbor_coordinates_2(dt, p,
std::back_inserter(coords));
if(!result.third){
std::cout << "The coordinate computation was not successful."
<< std::endl;
std::cout << "The point (" <<p << ") lies outside the convex hull."
<< std::endl;
}
K::FT norm = result.second;
std::cout << "Coordinate computation successful." << std::endl;
std::cout << "Normalization factor: " <<norm << std::endl;
std::cout << "done" << std::endl;
return 0;
}
69.1.4 Example for Regular Neighbor Coordinates
For regular neighbor coordinates, it is sufficient to replace the name of the function and the type of triangulation passed as parameter. A special traits class is needed.File: examples/Interpolation/rn_coordinates_2.cpp
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/Regular_triangulation_2.h>
#include <CGAL/Regular_triangulation_euclidean_traits_2.h>
#include <CGAL/regular_neighbor_coordinates_2.h>
typedef CGAL::Exact_predicates_inexact_constructions_kernel K;
typedef CGAL::Regular_triangulation_euclidean_traits_2<K> Gt;
typedef CGAL::Regular_triangulation_2<Gt> Regular_triangulation;
typedef Regular_triangulation::Weighted_point Weighted_point;
typedef std::vector< std::pair< Weighted_point, K::FT > >
Point_coordinate_vector;
int main()
{
Regular_triangulation rt;
for (int y=0 ; y<3 ; y++)
for (int x=0 ; x<3 ; x++)
rt.insert(Weighted_point(K::Point_2(x,y), 0));
//coordinate computation
Weighted_point wp(K::Point_2(1.2, 0.7),2);
Point_coordinate_vector coords;
CGAL::Triple<
std::back_insert_iterator<Point_coordinate_vector>,
K::FT, bool> result =
CGAL::regular_neighbor_coordinates_2(rt, wp,
std::back_inserter(coords));
if(!result.third){
std::cout << "The coordinate computation was not successful."
<< std::endl;
std::cout << "The point (" <<wp.point() << ") lies outside the convex hull."
<< std::endl;
}
K::FT norm = result.second;
std::cout << "Coordinate computation successful." << std::endl;
std::cout << "Normalization factor: " <<norm << std::endl;
std::cout << "done" << std::endl;
return 0;
}
For surface neighbor coordinates, the surface normal at the query
point must be provided, see Section 69.2.
69.2 Surface Natural Neighbor Coordinates and Surface Neighbors
This section introduces the functions to compute natural neighbor coordinates and surface neighbors associated to a set of sample points issued from a surface S and given a query point x on S. We suppose that S is a closed and compact surface of ℝ3, and let P= {p1, … ,pn} be an ε-sample of S (refer to Amenta and Bern [AB99]). The concepts are based on the definition of Boissonnat and Flötotto [BF02], [Flö03b]. Both references contain a thorough description of the requirements and the mathematical properties.
69.2.1 Introduction
Two observations lead to the definition of surface neighbors and surface neighbor coordinates: First, it is clear that the tangent plane Tx of the surface S at the point x ∈ S approximates S in the neighborhood of x. It has been shown in [BF02] that, if the surface S is well sampled with respect to the curvature and the local thickness of S, i.e. it is an ε-sample, the intersection of the tangent plane Tx with the Voronoi cell of x in the Voronoi diagram of P ∪ {x} has a small diameter. Consequently, inside this Voronoi cell, the tangent plane Tx is a reasonable approximation of S. Furthermore, the second observation allows to compute this intersection diagram easily: one can show using Pythagoras' Theorem that the intersection of a three-dimensional Voronoi diagram with a plane H is a two-dimensional power diagram. The points defining the power diagram are the projections of the points in P onto H, each point weighted with its negative square distance to H. Algorithms for the computation of power diagrams via the dual regular triangulation are well known and for example provided by Cgal in the class Regular_triangulation_2<Gt, Tds>.
69.2.2 Implementation
Voronoi Intersection Diagrams
In Cgal, the regular triangulation dual to the intersection of a 3D Voronoi diagram with a plane H can be computed by instantiating the Regular_triangulation_2<Gt, Tds> class with the traits class Voronoi_intersection_2_traits_3<K>. This traits class contains a point and a vector as class member which define the plane H. All predicates and constructions used by Regular_triangulation_2<Gt, Tds> are replaced by the corresponding operators on three-dimensional points. For example, the power test predicate (which takes three weighted 2D points p', q', r' of the regular triangulation and tests the power distance of a fourth point t' with respect to the power circle orthogonal to p, q, r) is replaced by a Side_of_plane_centered_sphere_2_3 predicate that tests the position of a 3D point t with respect to the sphere centered on the plane H passing through the 3D points p, q, r. This approach allows to avoid the explicit constructions of the projected points and the weights which are very prone to rounding errors.
Natural Neighbor Coordinates on Surfaces
The computation of natural neighbor coordinates on surfaces is based upon the computation of regular neighbor coordinates with respect to the regular triangulation that is dual to Vor(P) ∩ Tx, the intersection of Tx and the Voronoi diagram of P, via the function regular_neighbor_coordinates_2.
Of course, we might introduce all data points P into this regular triangulation. However, this is not necessary because we are only interested in the cell of x. It is sufficient to guarantee that all surface neighbors of the query point x are among the input points that are passed as argument to the function. The sample points P can be filtered for example by distance, e.g. using range search or k-nearest neighbor queries, or with the help of the 3D Delaunay triangulation since the surface neighbors are necessarily a subset of the natural neighbors of the query point in this triangulation. Cgal provides a function that encapsulates the filtering based on the 3D Delaunay triangulation. For input points filtered by distance, functions are provided that indicate whether or not points that lie outside the input range (i.e. points that are further from x than the furthest input point) can still influence the result. This allows to iteratively enlarge the set of input points until the range is sufficient to certify the result.
Surface Neighbors
The surface neighbors of the query point are its neighbors in the regular triangulation that is dual to Vor(P) ∩ Tx, the intersection of Tx and the Voronoi diagram of P. As for surface neighbor coordinates, this regular triangulation is computed and the same kind of filtering of the data points as well as the certification described above is provided.
69.2.3 Example for Surface Neighbor Coordinates
File: examples/Interpolation/surface_neighbor_coordinates_3.cpp
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/point_generators_3.h>
#include <CGAL/algorithm.h>
#include <CGAL/Origin.h>
#include <CGAL/surface_neighbor_coordinates_3.h>
typedef CGAL::Exact_predicates_inexact_constructions_kernel K;
typedef K::FT Coord_type;
typedef K::Point_3 Point_3;
typedef K::Vector_3 Vector_3;
typedef std::vector< std::pair< Point_3, K::FT > >
Point_coordinate_vector;
int main()
{
int n=100;
std::vector< Point_3> points;
points.reserve(n);
std::cout << "Generate " << n << " random points on a sphere."
<< std::endl;
CGAL::Random_points_on_sphere_3<Point_3> g(1);
CGAL::copy_n( g, n, std::back_inserter(points));
Point_3 p(1, 0,0);
Vector_3 normal(p-CGAL::ORIGIN);
std::cout << "Compute surface neighbor coordinates for "
<< p << std::endl;
Point_coordinate_vector coords;
CGAL::Triple< std::back_insert_iterator<Point_coordinate_vector>,
K::FT, bool> result =
CGAL::surface_neighbor_coordinates_3(points.begin(), points.end(),
p, normal,
std::back_inserter(coords),
K());
if(!result.third){
//Undersampling:
std::cout << "The coordinate computation was not successful."
<< std::endl;
return 0;
}
K::FT norm = result.second;
std::cout << "Testing the barycentric property " << std::endl;
Point_3 b(0, 0,0);
for(std::vector< std::pair< Point_3, Coord_type > >::const_iterator
it = coords.begin(); it!=coords.end(); ++it)
b = b + (it->second/norm)* (it->first - CGAL::ORIGIN);
std::cout <<" weighted barycenter: " << b <<std::endl;
std::cout << " squared distance: " <<
CGAL::squared_distance(p,b) <<std::endl;
std::cout << "done" << std::endl;
return 0;
}
69.3 Interpolation Methods
69.3.1 Introduction
Linear Precision Interpolation
Sibson [Sib81] defines a very simple interpolant that re-produces linear functions exactly. The interpolation of Φ(x) is given as the linear combination of the neighbors' function values weighted by the coordinates:
| Z0(x) = |
| . |
| Z0(x) = |
| = a+bt x |
Sibson's C1 Continuous Interpolant
In [Sib81], Sibson describes a second interpolation method that relies also on the function gradient gi for all pi ∈ P. It is C1 continuous with gradient gi at pi. Spherical quadrics of the form Φ(x) =a + bt x +γ xtx are reproduced exactly. The proof relies on the barycentric coordinate property of the natural neighbor coordinates and assumes that the gradient of Φ at the data points is known or approximated from the function values as described in [Sib81] (see Section 69.3.2).Sibson's Z1 interpolant is a combination of the linear interpolant Z0 and an interpolant ξ which is the weighted sum of the first degree functions
| ξi(x) = zi +git(x-pi), ξ(x)= |
| . |
| Z1(x) = |
| where α(x) = |
| and β(x)= |
| , |
Cgal contains a second implementation with f(||x - pi||) = ||x - pi||2 which is less demanding on the number type because it avoids the square-root computation needed to compute the distance ||x - pi||. The theoretical guarantees are the same (see [Flö03b]). Simply, the smaller the slope of f around f(0), the faster the interpolant approaches ξi as x → pi.
Farin's C1 Continuous Interpolant
Farin [Far90] extended Sibson's work and realizes a C1 continuous interpolant by embedding natural neighbor coordinates in the Bernstein-Bézier representation of a cubic simplex. If the gradient of Φ at the data points is known, this interpolant reproduces quadratic functions exactly. The function gradient can be approximated from the function values by Sibson's method [Sib81] (see Section 69.3.2) which is exact only for spherical quadrics.
Quadratic Precision Interpolants
Knowing the gradient gi for all pi ∈ P, we formulate a very simple interpolant that reproduces exactly quadratic functions. This interpolant is not C1 continuous in general. It is defined as follows:
| I1(x) = |
|
69.3.2 Gradient Fitting
Sibson describes a method to approximate the gradient of the function f from the function values on the data sites. For the data point pi, we determine| gi = min g |
| , |
Cgal provides functions to approximate the gradients of all data points that are inside the convex hull. There is one function for each type of natural neighbor coordinate (i.e. natural_neighbor_coordinates_2, regular_neighbor_coordinates_2).
69.3.3 Example for Linear Interpolation
File: examples/Interpolation/linear_interpolation_2.cpp
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/Delaunay_triangulation_2.h>
#include <CGAL/Interpolation_traits_2.h>
#include <CGAL/natural_neighbor_coordinates_2.h>
#include <CGAL/interpolation_functions.h>
typedef CGAL::Exact_predicates_inexact_constructions_kernel K;
typedef CGAL::Delaunay_triangulation_2<K> Delaunay_triangulation;
typedef CGAL::Interpolation_traits_2<K> Traits;
typedef K::FT Coord_type;
typedef K::Point_2 Point;
int main()
{
Delaunay_triangulation T;
std::map<Point, Coord_type, K::Less_xy_2> function_values;
typedef CGAL::Data_access< std::map<Point, Coord_type, K::Less_xy_2 > >
Value_access;
Coord_type a(0.25), bx(1.3), by(-0.7);
for (int y=0 ; y<3 ; y++)
for (int x=0 ; x<3 ; x++){
K::Point_2 p(x,y);
T.insert(p);
function_values.insert(std::make_pair(p,a + bx* x+ by*y));
}
//coordinate computation
K::Point_2 p(1.3,0.34);
std::vector< std::pair< Point, Coord_type > > coords;
Coord_type norm =
CGAL::natural_neighbor_coordinates_2
(T, p,std::back_inserter(coords)).second;
Coord_type res = CGAL::linear_interpolation(coords.begin(), coords.end(),
norm,
Value_access(function_values));
std::cout << " Tested interpolation on " << p << " interpolation: "
<< res << " exact: " << a + bx* p.x()+ by* p.y()<< std::endl;
std::cout << "done" << std::endl;
return 0;
}
69.3.4 Example for Sibson's C1 Interpolation Scheme with Gradient Estimation
File: examples/Interpolation/sibson_interpolation_2.cpp
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/Delaunay_triangulation_2.h>
#include <CGAL/natural_neighbor_coordinates_2.h>
#include <CGAL/Interpolation_gradient_fitting_traits_2.h>
#include <CGAL/sibson_gradient_fitting.h>
#include <CGAL/interpolation_functions.h>
typedef CGAL::Exact_predicates_inexact_constructions_kernel K;
typedef CGAL::Delaunay_triangulation_2<K> Delaunay_triangulation;
typedef CGAL::Interpolation_gradient_fitting_traits_2<K> Traits;
typedef K::FT Coord_type;
typedef K::Point_2 Point;
typedef std::map<Point, Coord_type, K::Less_xy_2> Point_value_map ;
typedef std::map<Point, K::Vector_2 , K::Less_xy_2 > Point_vector_map;
int main()
{
Delaunay_triangulation T;
Point_value_map function_values;
Point_vector_map function_gradients;
//parameters for spherical function:
Coord_type a(0.25), bx(1.3), by(-0.7), c(0.2);
for (int y=0 ; y<4 ; y++)
for (int x=0 ; x<4 ; x++){
K::Point_2 p(x,y);
T.insert(p);
function_values.insert(std::make_pair(p,a + bx* x+ by*y + c*(x*x+y*y)));
}
sibson_gradient_fitting_nn_2(T,std::inserter(function_gradients,
function_gradients.begin()),
CGAL::Data_access<Point_value_map>
(function_values),
Traits());
//coordiante computation
K::Point_2 p(1.6,1.4);
std::vector< std::pair< Point, Coord_type > > coords;
Coord_type norm =
CGAL::natural_neighbor_coordinates_2(T, p,std::back_inserter
(coords)).second;
//Sibson interpolant: version without sqrt:
std::pair<Coord_type, bool> res =
CGAL::sibson_c1_interpolation_square
(coords.begin(),
coords.end(),norm,p,
CGAL::Data_access<Point_value_map>(function_values),
CGAL::Data_access<Point_vector_map>(function_gradients),
Traits());
if(res.second)
std::cout << " Tested interpolation on " << p
<< " interpolation: " << res.first << " exact: "
<< a + bx * p.x()+ by * p.y()+ c*(p.x()*p.x()+p.y()*p.y())
<< std::endl;
else
std::cout << "C^1 Interpolation not successful." << std::endl
<< " not all function_gradients are provided." << std::endl
<< " You may resort to linear interpolation." << std::endl;
std::cout << "done" << std::endl;
return 0;
}
An additional example in the distribution compares numerically the errors of the different interpolation functions with respect to a known function. It is distributed in the examples directory.