This repository contains the PyTorch implementation of these papers:

SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator
Zhe Zhu, Honghua Chen, Xing He, Weiming Wang, Jing Qin, Mingqiang Wei
ICCV 2023

PointSea: Point Cloud Completion via Self-structure Augmentation
Zhe Zhu, Honghua Chen, Xing He, Mingqiang Wei
IJCV 2025, journal extension of SVDFormer

In this paper, we propose a novel network, SVDFormer, to tackle two specific challenges in point cloud completion: understanding faithful global shapes from incomplete point clouds and generating high-accuracy local structures. Current methods either perceive shape patterns using only 3D coordinates or import extra images with well-calibrated intrinsic parameters to guide the geometry estimation of the missing parts. However, these approaches do not always fully leverage the cross-modal self-structures available for accurate and high-quality point cloud completion. To this end, we first design a Self-view Fusion Network that leverages multiple-view depth image information to observe incomplete self-shape and generate a compact global shape. To reveal highly detailed structures, we then introduce a refinement module, called Self-structure Dual-generator, in which we incorporate learned shape priors and geometric self-similarities for producing new points. By perceiving the incompleteness of each point, the dual-path design disentangles refinement strategies conditioned on the structural type of each point. SVDFormer absorbs the wisdom of self-structures, avoiding any additional paired information such as color images with precisely calibrated camera intrinsic parameters. Comprehensive experiments indicate that our method achieves state-of-the-art performance on widely-used benchmarks.

We provide pretrained SVDFormer models on PCN and ShapeNet-55/34 here.

• python >= 3.6
• PyTorch >= 1.8.0
• CUDA >= 11.1
• easydict
• opencv-python
• transform3d
• h5py
• timm
• open3d
• tensorboardX

Install PointNet++ and Density-aware Chamfer Distance.

cd pointnet2_ops_lib
python setup.py install
cd ../metrics/CD/chamfer3D/
python setup.py install
cd ../../EMD/
python setup.py install

Download the PCN and ShapeNet55/34 datasets, and specify the data path in config_*.py (pcn/55).

# PCN
__C.DATASETS.SHAPENET.PARTIAL_POINTS_PATH        = '/path/to/ShapeNet/%s/partial/%s/%s/%02d.pcd'
__C.DATASETS.SHAPENET.COMPLETE_POINTS_PATH       = '/path/to/ShapeNet/%s/complete/%s/%s.pcd'
# ShapeNet-55
__C.DATASETS.SHAPENET55.COMPLETE_POINTS_PATH     = '/path/to/shapenet_pc/%s'
# Switch to ShapeNet-34 Seen/Unseen
__C.DATASETS.SHAPENET55.CATEGORY_FILE_PATH       = '/path/to/datasets/ShapeNet34(ShapeNet-Unseen21)'

# Specify the checkpoint path in config_*.py
__C.CONST.WEIGHTS = "path to your checkpoint"
python main_*.py --test (pcn/55)

python main_*.py (pcn/55) 

@InProceedings{SVDFormer,
    author    = {Zhu, Zhe and Chen, Honghua and He, Xing and Wang, Weiming and Qin, Jing and Wei, Mingqiang},
    title     = {SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {14508-14518}
}
@article{PointSea,
  title={PointSea: Point Cloud Completion via Self-structure Augmentation},
  author={Zhu, Zhe and Chen, Honghua and He, Xing and Wei, Mingqiang},
  journal={International Journal of Computer Vision},
  volume={133},
  number={7},
  pages={4770--4794},
  year={2025}
}

The repository is based on SnowflakeNet, some of the code is borrowed from:

• PytorchPointNet++
• Density-aware Chamfer Distance
• PoinTr
• GRNet
• PointAttN
• SimpleView

The point clouds are visualized with Easy3D.

We thank the authors for their great work！

This project is open sourced under MIT license.