ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds

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.

Paper | Project Page

Installation

To install conda environment:

conda env create --force environment.yml -n parsenet
source activate parsenet

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Dataset

To dowload the dataset, run:

bash download_dataset.sh

For data organization, please see readme_data.md.

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Experiments

Experiments are done on Nvidia 1080ti gpus.

SplineNet

• 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

ParSeNet

• 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

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Acknowledgements

1. This project takes inspiration of designing network architecture from the code base provided by Wang et.al.: https://github.com/WangYueFt/dgcnn
2. We also thank Sebastian for timely release and advice on ABC dataset: https://deep-geometry.github.io/abc-dataset/

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Citation

@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}
}