The official repository of the paper "Hyperbolic Chamfer Distance for Point Cloud Completion" published at ICCV 2023

Our code and model weights will be released soon!!!

We update SeedFormer + HyperCD in Aug 23th

The code has been tested on one configuration:

• python == 3.6.8
• PyTorch == 1.8.1
• CUDA == 10.2
• numpy
• open3d

pip install -r requirements.txt

Compile the C++ extension modules:

sh install.sh

The details of used datasets can be found in DATASET.md

First, you should specify your dataset directories in train_pcn.py:

__C.DATASETS.SHAPENET.PARTIAL_POINTS_PATH        = '<*PATH-TO-YOUR-DATASET*>/PCN/%s/partial/%s/%s/%02d.pcd'
__C.DATASETS.SHAPENET.COMPLETE_POINTS_PATH       = '<*PATH-TO-YOUR-DATASET*>/PCN/%s/complete/%s/%s.pcd'

To train SeedFormer + HyperCD on PCN dataset, simply run:

python3 train_pcn.py

To test a pretrained model, run:

python3 train_pcn.py --test

Or you can give the model directory name to test one particular model:

python3 train_pcn.py --test --pretrained train_pcn_Log_2022_XX_XX_XX_XX_XX

Save generated complete point clouds as well as gt and partial clouds in testing:

python3 train_pcn.py --test --output 1

To use ShapeNet55 dataset, change the data directoriy in train_shapenet55.py:

__C.DATASETS.SHAPENET55.COMPLETE_POINTS_PATH     = '<*PATH-TO-YOUR-DATASET*>/ShapeNet55/shapenet_pc/%s'

Then, run:

python3 train_shapenet55.py

In order to switch to ShapeNet34, you can change the data file in train_shapenet55.py:

__C.DATASETS.SHAPENET55.CATEGORY_FILE_PATH       = './datasets/ShapeNet55-34/ShapeNet-34/'

The testing process is very similar to that on PCN:

python3 train_shapenet55.py --test

Code is borrowed from SeedFormer, HyperCD loss can be found in loss_utils.py, This loss can be easily implement to other networks such as PointAttN and CP-Net.

Please cite our papers if you use our idea or code:

@InProceedings{Lin_2023_ICCV,
    author    = {Lin, Fangzhou and Yue, Yun and Hou, Songlin and Yu, Xuechu and Xu, Yajun and Yamada, Kazunori D and Zhang, Ziming},
    title     = {Hyperbolic Chamfer Distance for Point Cloud Completion},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {14595-14606}
}