The objective of the project is to explicitly label the mesh into its components. The problem of mesh labelling can be viewed a classification problem as such this project trains a random forest classifier trained on mesh data generated using a ensemble of mesh descriptors which effectively and concisely capture the surface,volume and orientation of the mesh.

1. Average Geodesic Distance (Local surface property)

Approximate average geodesic distance using fast marching algorithm.

2. Shape diameter function(Local Volume property)

Approximation method calculate the distance from surface to the medial axis. Uses Moller-Trumbore algorithm for triangle ray intersection.

3. Curvature (Gaussian,Mean and principle curvatures)(1-Ring neighbourhood surface property)

First order differential attributes on a piecewise linear triangular mesh.

4. Volumetric shape images(Local Volume property)

Better approximation of distance from surface to the medial axis. A Two pass algorithm which internally uses SDF.

5. Shape Context(Surface and mesh face orientation property)

Combines geodesic distance with the orientation by computing the angle between the surface normal and line connecting the face centres.

Time to generate each features

Feature wise and Part wise precision

Please Checkout PresentationFolder for detailed report.