Added Laplacian smoothing to shape feature code.
Added laplacian spectral compression to shape features.
Added ability to compress a point cloud at all possible levels and write to disk for compiling into a movie.
Something like the heat kernel kind of.
A few steps more in getting HKS implemented. Need to figure out the right use of A.
Need to write a visualization to check if it is working.
Visualize heat kernel distances.
Turning off print statements.
Putting this back in.
Heisenbug.
Switched to Eigen library instead of opencv eigenvalue decomposition.
Use a distance weighted graph laplacian for smooothing.
Something kind of approaching heat kernel signature.
Transform the time points out of the log space.
Remove lambda_norm.
Fixed time values for shape feature extraction.
Prettier colors in the output for HKS comparison.
Different Laplacian seems to help, need to figure out the correct one.
Remove first (smallest) eigenvalue from computation.
Visualize pairwise distances for all points in a distance matrix. Shows that something is wrong in the distance calculation.
Fixed HKS distance initialization to give correct answers.
Changed visualization slightly.
Changed Graph Laplacian to use inverse distance, will examine different methods and make interchangable shortly.
Broke out Laplacian matrix construction into separate functions for easier reuse and comparison / swapping of different Laplacian forms.
converted hull smoothing to use eigen datastructures. Now works for arbitrary smoothing levels.
Write HKS dists to disk for video compilation and analysis.
Normalize eigenvectors for HKS computation.
Added more laplacians.