This repository contains code for the neural implicit reconstruction experiments in the paper Vector Neurons: A General Framework for SO(3)-Equivariant Networks. Code for classification and segmentation experiments can be found here.
The code structure follows Occupancy Networks. Please follow their instructions to prepare the data and install the dependencies. Run
python generate_random_rotation.py
to precompute the random rotations for all input pointclouds.
To train and evaluate the networks, please run these two commands
python train.py CONFIG.yaml
python eval.py CONFIG.yaml
The configuration files are, for VN-OccNet,
configs/equinet/vnn_pointnet_resnet_resnet_ROTATION.yaml
for the vanilla OccNet baseline,
configs/pointcloud/onet_resnet_ROTATION.yaml
and for vanilla PointNet encoder + invariant decoder,
configs/equinet/inner_baseline_resnet_ROTATION.yaml
ROTATION can be chosen from aligned (no rotations) and so3 (with precomputed random rotations). We also provide two settings rot-rand (generate random rotations on the fly during training) and pca (apply PCA pre-alignment the the input pointclouds), which are not reported in the paper.
Please cite this paper if you want to use it in your work,
@article{deng2021vn,
title={Vector Neurons: a general framework for SO(3)-equivariant networks},
author={Deng, Congyue and Litany, Or and Duan, Yueqi and Poulenard, Adrien and Tagliasacchi, Andrea and Guibas, Leonidas},
journal={arXiv preprint arXiv:2104.12229},
year={2021}
}
MIT License
The structure of this codebase is borrowed from Occupancy Networks.