This repository contains the PyTorch implementation of the paper:

FSC: Few-point Shape Completion, CVPR 2024

Xianzu Wu, Xianfeng Wu, Tianyu Luan, Yajing Bai, Zhongyuan Lai, Junsong Yuan.

While previous studies have demonstrated successful 3D object shape completion with a sufficient number of points, they often fail in scenarios when a few points, e.g. tens of points, are observed. Surprisingly, via entropy analysis, we find that even a few points, e.g. 64 points, could retain substantial information to help recover the 3D shape of the object. To address the challenge of shape completion with very sparse point clouds, we then propose Few-point Shape Completion (FSC) mode, which contains a novel dual-branch feature extractor for handling extremely sparse inputs, coupled with an extensive branch for maximal point utilization with a saliency branch for dynamic importance assignment. This model is further bolstered by a two-stage revision network that refines both the extracted features and the decoder output, enhancing the detail and authenticity of the completed point cloud. Our experiments demonstrate the feasibility of recovering 3D shapes from a few points. The proposed Few-point Shape Completion (FSC) mode model outperforms previous methods on both few-point inputs and many-point inputs, and shows good generalizability to different object categories.

We provide pretrained FSC models(CVRR2024) PCN_Pretrained_Model password: oq29

• 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)'

Test Unseen Need this dataset, Download the PCN.

python main_pcn.py --test True --test_dataset_path /mnt/data/PCN(your own dataset path) --ckpt_path /mnt/data/checkpoint/FSC_best_PCN.pth(your model path) --novel True(True: Unsenn, Fasle: Seen)

python main_*.py (pcn/pcn_base) 

@InProceedings{wu2024fsc,
  title={FSC: Few-point Shape Completion},
  author={Wu, Xianzu and Wu, Xianfeng and Luan, Tianyu and Bai, Yajing and Lai, Zhongyuan and Yuan, Junsong},
  journal={arXiv preprint arXiv:2403.07359},
  year={2024}
}

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

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

The point clouds are visualized with Easy3D.

We thank the authors for their great work！