MVP Benchmark: Multi-View Partial Point Clouds for Completion and Registration

[NEWS]

• 2021-07-12 [NEW 🎉 ] The submission on Codalab starts!
• 2021-07-10 [NEW 🎉 ] Database website and Challenge website are online!
• 2021-07-09 [NEW 🎉 ] Code and data released!
• 2021-07-01 The MVP challenges will be hosted in the ICCV2021 Workshop: Sensing, Understanding and Synthesizing Humans.

[Timeline]

• 2021-07-12   Submission start date
• 2021-09-12   Public submission deadline
• 2021-09-19   Private submission deadline
• 2021-10-04   Technical report deadline
• 2021-10-17   Awards at ICCV2021 Workshop

[MVP Benchmark]

Overview

This repository introduces the MVP Benchmark for partial point cloud COMPLETION and REGISTRATION, and it also includes following recent methods:

• Completetion:   [1] PCN;   [2] ECG;   [3] VRCNet

• Registration:   [1] DCP;   [2] DeepGMR;   [3] IDAM

This repository is implemented in Python 3.7, PyTorch 1.5.0, CUDA 10.1 and gcc > 5.

Installation

Install Anaconda, and then use the following command:

git clone --depth=1 https://github.com/paul007pl/MVP_Benchmark.git
cd MVP_Benchmark; source setup.sh;

If your connection to conda and pip is unstable, it is recommended to manually follow the setup steps in setup.sh.

MVP Dataset

Download corresponding dataset:

• Completion :     Google Drive    or    百度网盘  (code: p364)
• Registration :     Google Drive    or    百度网盘  (code: p364)

Usage

For both completion and registration:

• cd completion or cd registration

• To train a model: run python train.py -c ./cfgs/*.yaml, e.g. python train.py -c ./cfgs/pcn.yaml

• To test a model: run python test.py -c ./cfgs/*.yaml, e.g. python test.py -c ./cfgs/pcn.yaml

• Config for each algorithm can be found in cfgs/.

• run_train.sh and run_test.sh are provided for SLURM users.

• Different partial point clouds for the same CAD Model:

• High-quality complete point clouds:

[Citation]

If you find our code useful, please cite our paper:

@article{pan2021variational,
  title={Variational Relational Point Completion Network},
  author={Pan, Liang and Chen, Xinyi and Cai, Zhongang and Zhang, Junzhe and Zhao, Haiyu and Yi, Shuai and Liu, Ziwei},
  journal={arXiv preprint arXiv:2104.10154},
  year={2021}
}

[License]

Our code is released under Apache-2.0 License.

[Acknowledgement]

We include the following PyTorch 3rd-party libraries:
[1] CD
[2] EMD
[3] MMDetection3D

We include the following algorithms:
[1] PCN
[2] ECG
[3] VRCNet
[4] DCP
[5] DeepGMR
[6] IDAM