This repository contains source code for Hyperspherical Embedding for Point Cloud Completion (CVPR 2023).
Download the datasets for point cloud completion: ModelNet40, Completion3D, MVP, ShapeNet. Fill in the corresponding data path in run.sh
.
Check the docker files in docker/
and build docker image:
cd docker/
./build.sh
Create docker container using one GPU
./run.sh 0
Check the config file cfgs/config.yaml
each time you run the experiment.
./train.sh
task
in cfgs/config.yaml
to be a list of the desired tasks. For example, to train on both classification and comletion tasks, set task
to be ['classification','completion']
.Keep all the config parameters as training, and set eval
to True
, and then run:
./train.sh
Set the --logdir
in tensorboard.sh
to be the desired log directory and run:
./tensorboard.sh
If you find this work useful for your research, please cite HyperPC in your publications.
@InProceedings{Zhang_2023_CVPR,
author = {Zhang, Junming and Zhang, Haomeng and Vasudevan, Ram and
Johnson-Roberson, Matthew},
title = {Hyperspherical Embedding for Point Cloud Completion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {5323-5332}
}