MATLAB Examples

Quadtree point search in a triangulation

Given a triangulation, we want to find the indices of the triangles containing some query points, along with their respective barycentric coordinates. We compare the computation times for the pointLocation and pointLocationQuadTree algorithms depending on the size of the search problem, setting the number query points equal to the number of triangulation points.



close all

nPoints = 4.^(1:7);

Start computation loop

for i=1:length(nPoints)

Test data

Delaunay triangulation of a random point set.

  delaunayTri = delaunayTriangulation(rand(nPoints(i),2));
  nTriangles(i) = size(delaunayTri,1);

Standard triangulation of the same set, neglecting the Delaunay property.

  standardTri = triangulation(delaunayTri.ConnectivityList, ...

Random set of query points.

  queryPoints = rand(nPoints(i),2);

Matlab search in a Delaunay triangulation

The Matlab search in a Delaunay triangulation is very fast. We will not be able to beat it.

  [indMatlabDelaunay,barMatlabDelaunay] = pointLocation(delaunayTri,queryPoints);
  tictocMatlabDelaunay(i) = toc; %#ok<*SAGROW>

Matlab search in a standard triangulation

As of Matlab R2018a, the Matlab search in a standard triangulation is quite slow for large triangulations and a large numbers of query points. (Think of interpolating finite element solutions.)

  [indMatlabStandard,barMatlabStandard] = pointLocation(standardTri,queryPoints);
  tictocMatlabStandard(i) = toc;

Quadtree search in a Delaunay triangulation

Quadtree search works with Delaunay triangulations, but directly calls the standard Matlab search in a Delaunay triangulation.

  [indQuadtreeDelaunay,barQuadtreeDelaunay] = pointLocationQuadTree(delaunayTri,queryPoints);
  tictocQuadtreeDelaunay(i) = toc;

Quadtree search in a standard triangulation

Quadtree search in a standard triangulation is much faster than Matlab search in a standard triangulation if the triangulation and number of query points are large.

  [indQuadtreeStandard,barQuadtreeStandard] = pointLocationQuadTree(standardTri,queryPoints);
  tictocQuadtreeStandard(i) = toc;

Test for correct results

Check that all methods find exactly the same triangle indices. The case that a random query point is exactly on the edge between two triangles is rare and, therefore, not taken care of.


The barycentric coordinates are subject to round-off, so we can not expect exact equality of the results. Checking against an absolute tolerance is sufficient for this test.

  tolerance = 1e-12;
  pick = ~isnan(indMatlabDelaunay);
  if any(pick)  % assert() does not work for empty inputs
    approximationError = @(x)max(max(abs(barMatlabDelaunay(pick,:)-x(pick,:))));

Terminate computation loop


Display results

The slopes suggest that the cost of the Matlab search in a standard triangulation depends about quadratically on the problem size, while the Quadtree search in a standard triangulation depends about linearly on the problem size. The vertical dotted line indicates the point at which pointLocationQuadTree actually builds a quadtree. For smaller sizes, pointLocationQuadTree directly calls pointLocation.

loglog(nPoints,tictocMatlabStandard,'-s','DisplayName','Matlab (standard)')
hold on
loglog(nPoints,tictocQuadtreeStandard,'-x','DisplayName','Quadtree (standard)')
loglog(nPoints,tictocQuadtreeDelaunay,'-o','DisplayName','Quadtree (Delaunay)')
loglog(nPoints,tictocMatlabDelaunay,'-^','DisplayName','Matlab (Delaunay)')
xlabel('n = number of query points = number of mesh points')
ylabel('computation time in seconds')
title('quadtree vs. standard search')

threshold = find(nPoints.*nTriangles >= 1e6,1,'first');
pick = threshold:length(nPoints);

if length(pick)>1

  quadraticSlope = 2*tictocMatlabStandard(end)*(nPoints(pick)/nPoints(end)).^2;

  linearSlope = 0.5*tictocQuadtreeStandard(end)*(nPoints(pick)/nPoints(end)).^1;

  loglog(nPoints(threshold)*[1 1],get(gca,'YLim'),'k:','DisplayName','Quadtree threshold');