• 2022-03 [NEW:tada:] The Jittor implementations of both PMP-Net and PMP-Net++ are released in the PMPPlus-Jittor folder.
• 2022-02 [NEW:tada:] PMP-Net++, the journal extension of PMP-Net is accepted to TPAMI. This repository now contains the code of both PMP-Net and PMP-Net++!
• 2021 PMP-Net is published at CVPR 2021, and the code is released!

This repository contains the PyTorch implementation and Jittor implementation of the papers:

1. PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths, TPAMI 2023

2. PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths, CVPR 2021

[ PMP-Net | PMP-Net++ | IEEE Xplore | Webpage | Jittor ]

Point cloud completion concerns to predict missing part for incomplete 3D shapes. A common strategy is to generate complete shape according to incomplete input. However, unordered nature of point clouds will degrade generation of high-quality 3D shapes, as detailed topology and structure of unordered points are hard to be captured during the generative process using an extracted latent code. We address this problem by formulating completion as point cloud deformation process. Specifically, we design a novel neural network, named PMP-Net++, to mimic behavior of an earth mover. It moves each point of incomplete input to obtain a complete point cloud, where total distance of point moving paths (PMPs) should be the shortest. Therefore, PMP-Net++ predicts unique PMP for each point according to constraint of point moving distances. The network learns a strict and unique correspondence on point-level, and thus improves quality of predicted complete shape. Moreover, since moving points heavily relies on per-point features learned by network, we further introduce a transformer-enhanced representation learning network, which significantly improves completion performance of PMP-Net++. We conduct comprehensive experiments in shape completion, and further explore application on point cloud up-sampling, which demonstrate non-trivial improvement of PMP-Net++ over state-of-the-art point cloud completion/up-sampling methods

@ARTICLE{pmpnet++,
  author={Wen, Xin and Xiang, Peng and Han, Zhizhong and Cao, Yan-Pei and Wan, Pengfei and Zheng, Wen and Liu, Yu-Shen},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-Step Point Moving Paths}, 
  year={2023},
  volume={45},
  number={1},
  pages={852-867},
  doi={10.1109/TPAMI.2022.3159003}}

@inproceedings{wen2021pmp,
    title={PMP-Net: Point cloud completion by learning multi-step point moving paths},
    author={Wen, Xin and Xiang, Peng and Han, Zhizhong and Cao, Yan-Pei and Wan, Pengfei and Zheng, Wen and Liu, Yu-Shen},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2021}
}

We use the PCN and Compeletion3D datasets in our experiments, which are available below:

• PCN
• Completion3D

The pretrained models on Completion3D and PCN dataset are available as follows:

• PMP-Net_pre-trained

Backup Links:

• PMP-Net_pre-trained (pwd: n7t4)

cd PMP-Net
conda create -n pmp python=3.7
conda activate pmp
pip3 install -r requirements.txt

NOTE: PyTorch >= 1.4 of cuda version are required.

cd pointnet2_ops_lib
python setup.py install

cd ..

cd Chamfer3D
python setup.py install

You need to update the file path of the datasets:

__C.DATASETS.COMPLETION3D.PARTIAL_POINTS_PATH    = '/path/to/datasets/Completion3D/%s/partial/%s/%s.h5'
__C.DATASETS.COMPLETION3D.COMPLETE_POINTS_PATH   = '/path/to/datasets/Completion3D/%s/gt/%s/%s.h5'
__C.DATASETS.SHAPENET.PARTIAL_POINTS_PATH        = '/path/to/datasets/ShapeNet/ShapeNetCompletion/%s/partial/%s/%s/%02d.pcd'
__C.DATASETS.SHAPENET.COMPLETE_POINTS_PATH       = '/path/to/datasets/ShapeNet/ShapeNetCompletion/%s/complete/%s/%s.pcd'

# Dataset Options: Completion3D, Completion3DPCCT, ShapeNet, ShapeNetCars
__C.DATASET.TRAIN_DATASET                        = 'ShapeNet'
__C.DATASET.TEST_DATASET                         = 'ShapeNet'

To train PMP-Net++ or PMP-Net, you can simply use the following command:

python main_*.py  # remember to change '*' to 'c3d' or 'pcn', and change between 'import PMPNetPlus' and 'import PMPNet'

To test or inference, you should specify the path of checkpoint if the config_*.py file

__C.CONST.WEIGHTS                                = "path to your checkpoint"

then use the following command:

python main_*.py --test
python main_*.py --inference

Some of the code of this repo is borrowed from GRNet, pytorchpointnet++ and ChamferDistancePytorch. We thank the authors for their wonderful job!

This project is open sourced under MIT license.