ExMeshCNN

ExMeshCNN: An Explainable Convolutional Neural Network Architecture for 3D Shape Analysis

Accepted in ACM SIGKDD 2022 (https://kdd.org/kdd2022/)

Requirements

• ubuntu 20.04.3 LTS

• CUDA == 11.1

• cudnn == 8.1.0

• numpy == 1.19.5

• pymeshlab == 2021.7

• torch == 1.7.1+cu110

• torchvision == 0.8.2+cu110

• trimesh == 3.9.29

Dataset

• SHREC (https://github.com/ranahanocka/MeshCNN)

• Cubes (https://github.com/ranahanocka/MeshCNN)

• Manifold40 (https://github.com/lzhengning/SubdivNet)

• ModelNet40 (https://modelnet.cs.princeton.edu/)

• Download: https://drive.google.com/file/d/19Typ-Yt1oBDwr9q5uCawa6EHqsJMH-Vn/view?usp=sharing

  • These datasets were created to have an equal number of faces and follow a manifold format.

Usage

• Preprocessing: python resize_manifold.py && python make_dataset.py
• Train the model: python trainer.py
• Explain: python Grad-CAM.py

code description

• resize_manifold.py
  • Make it follow the manifold format and have the same number of faces.
• make_dataset.py
  • It transforms into the data structure suggested in the paper to efficiently conduct learning and testing.
• trainer.py
  • Train the model.
• Grad-CAM.py
  • Describe the model using the GradCAM method.

Hyperparameters are defined in the code.

For proper testing, hyperparameters of each file must be modified.