# Visualizing samples on a sphere

## Alec Jacobson

## May 17, 2013

In my project, I need to uniformly sample directions or equivalently points on the unit sphere. A correct way is to sample the azimuth and cosine of the polar angle uniformly. Another way is to sample points in ℝ^{3} randomly with mean at the origin and variance 1.
One naive *non uniform* way, is to sample the x, y and z between [-1,1] uniformly and normalize. I knew that this was biased, but I wanted to *see* the bias. Here's a small matlab program that continuous splats random points onto a textured sphere:
```
% Can't use sphere(); have to use funny parameterization so tex-mapping works
n = 100;
theta = (-n:2:n)/n*pi;
sinphi = (-n:2:n)'/n*1;
cosphi = sqrt(1-sinphi.^2); cosphi(1) = 0; cosphi(n+1) = 0;
sintheta = sin(theta); sintheta(1) = 0; sintheta(n+1) = 0;
X = cosphi*cos(theta);
Y = cosphi*sintheta;
Z = sinphi*ones(1,n+1);
% square texture resolution
w = 1000;
im = ones(w,w);
s = surf(X,Y,Z,'FaceColor','texturemap','Cdata',im,'EdgeColor','none');
axis equal;
colormap(gray(255));
method = 'naive';
%method = 'uniform';
%method = 'normal-deviation';
it = 0;
sam = 2000000;
while true
switch method
case 'naive'
% uniformly random 3d point, each coord in [-1,1] and normalize
N = normalizerow(2*rand(sam,3)-1);
case 'normal-deviation'
N = normalizerow(normrnd(zeros(sam,3),1));
case 'uniform'
% random polar angle and azimuth
Z = rand(sam,1)*2-1;
A = rand(sam,1)*2*pi;
R = sqrt(1-Z.^2);
N = [Z R.*cos(A) R.*sin(A)];
end
% project
S = [(N(:,3)+1)/2 (atan2(N(:,2),N(:,1))+pi)/(2*pi)];
% splat!
S = round([mod(w*S(:,2),w-0.5) mod(w*S(:,1),w-0.5)]+1);
im = im + full(1-sparse(S(:,2),S(:,1),1,w,w));
set(s,'CData',matrixnormalize(im));
it = it + 1;
drawnow;
end
```

This produces an incrementally improving image of a textured sphere where black means high probability and white means low probability:
When this converges we can examine the bias around the sphere:
The "converged" texture map looks like:
Of course, a correct uniform sampling converges rather boringly to a uniformly black sphere.
In a different project we need not only a random sampling on the sphere, but also a Delaunay triangle mesh. Since all points lie on the sphere they also lie on their respective convex hull:
```
V = normalizerow(normrnd(zeros(sam,3),1));
F = convhulln(V);
```

The result looks something like this: