Authors: Gopal Sharma, Difan Liu, Evangelos Kalogerakis, Subhransu Maji, Siddhartha Chaudhuri, Radomír Měch
This repository contains codebase for the ParSeNet paper published at ECCV-2020.
To install conda environment:
conda env create --force environment.yml -n parsenet
source activate parsenetTo dowload the dataset, run:
bash download_dataset.shFor data organization, please see readme_data.md.
Experiments are done on Nvidia 1080ti gpus.
- To train open SplineNet (with 2 gpus):
python train_open_splines.py configs/config_open_splines.yml- To test open SplineNet:
python test_open_splines.py configs/config_test_open_splines.yml- To train closed SplineNet (with 2 gpus):
python train_closed_control_points.py configs/config_closed_splines.yml- To test closed SplineNet:
python test_closed_control_points.py configs/config_test_closed_splines.yml- To train ParseNet with only points as input (with 4 gpus):
python train_parsenet.py configs/config_parsenet.yml
- To train ParseNet with points and normals as input (with 4 gpus):
python train_parsenet.py configs/config_parsenet_normals.yml
- To train ParseNet in an end to end manner (note that you need to first pretrain the above models), then specify the path to the trained model in
configs/config_parsenet_e2e.yml(with 2 gpus). Further note that, this part of the training requires dynamic amount of gpu memory because a shape can have variable number of segment and corresponding number of fitting module. Training is done using Nvidia m40 (24 Gb gpu).
python train_parsenet_e2e.py configs/config_parsenet_e2e.yml
- Testing can be done using
test.py
python test.py 0 3998
- This project takes inspiration of designing network architecture from the code base provided by Wang et.al.: https://github.com/WangYueFt/dgcnn
- We also thank Sebastian for timely release and advice on ABC dataset: https://deep-geometry.github.io/abc-dataset/
@misc{sharma2020parsenet,
title={ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds},
author={Gopal Sharma and Difan Liu and Evangelos Kalogerakis and Subhransu Maji and Siddhartha Chaudhuri and Radomír Měch},
year={2020},
eprint={2003.12181},
archivePrefix={arXiv},
primaryClass={cs.CV}
}