MVP Benchmark for Multi-View Partial Point Cloud Completion and Registration

Overview

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

[NEWS]

[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:

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:

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

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