height-persist

3D object classification using the Persistent Homology Transform (PHT).

This project contains code for classifying 3D objects in the form of CAD models using
Persistent homology transform (PHT). More information about the PHT can be found in the paper Persistent homology transform for modeling shapes and surfaces.

examples

A few CAD models along with their PHT representation.

                 

                 

Files

core

Contains the core code for converting CAD models into persistence diagrams.

• core/surface.py: This file converts the CAD models from obj format and loads it into the simplextree data structure, computes and stores the persistence diagrams in the features folder.

• core/sphere_sampler.py: Functions for sampling uniform evenly distributed directions from the sphere.

• core/obj_utils.py: Loads obj files into python.

• core/off_utils.py: Loads offf files into python.

scripts

Contains various scripts for preprocessing the CAD models, generating the persistence diagrams and passing them through standard ML pipelines.

• scripts/generate_features.py: Takes a folder containing a dataset of 3D CAD models and outputs the corresponding persistence diagrams inside a features folder maintaining the directory structure of the dataset.

• scripts/install_script.bash: This script needs to be run to install the core functions to be used by the scripts.

• scripts/randomforest: Randomforest classifier for the ModelNet40 dataset.

• scripts/pytorch: Neural network classifier for the ModelNet40 dataset (in progress).

• scripts/dataprep: Various tools for preprocessing the CAD models.

test

Various simple test routines for the functions in core.

Notes

• The CAD models are preprocessed by reducing the number of faces to 2000 and converting to manifolds.
• We obtain around 80% test set accuracy in the ModelNet40 dataset using a simple RandomForest classifier with 100 nodes.