FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction

Paper

⚙️ Installation

Our system uses CUDA10.1. Setup the environment with following commands:

conda create --name fvor python=3.8.0
conda activate fvor
conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.1 -c pytorch
pip install -r requirements.txt

python setup.py build_ext --inplace

cd ./src/lib/sdf_extension
python setup.py install
cd ../../../

:file_folder: Download

Our ShapeNet dataset are based on Occupancy Network. Please go to Occupancy Network and download their processed data. And also download and uncompress our processed data and index file. You should make a folder structure as follows:

.
└── data
    └── shapenet
        ├── FvOR_ShapeNet
        │   └── 03001627
        │       └── ae02a5d77184ae2638449598167b268b
        ├── index
        │   ├── data
        │   │   └── 03001627_ae02a5d77184ae2638449598167b268b.npz
        │   ├── test.txt
        │   ├── train.txt
        │   └── val.txt
        └── ShapeNet   <- Occupancy Network's processed data
            └── 03001627
                └── ae02a5d77184ae2638449598167b268b

• ShapeNet checkpoints shape initialization, pose initialization, joint optimization

We also test our approach on HM3D-ABO dataset. Please follow the instructions in HM3D-ABO to setup the dataset.

• HM3D-ABO checkpoints shape initialization, pose initialization, joint optimization

⏳ ShapeNet

⏳ HM3D-ABO

If you find our work useful for your research, please consider citing our paper:

@misc{yang2022fvor,
      title={FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction}, 
      author={Zhenpei Yang and Zhile Ren and Miguel Angel Bautista and Zaiwei Zhang and Qi Shan and Qixing Huang},
      year={2022},
      eprint={2205.07763},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Acknowledgement

We thank the awesome codes from LoFTR, Occupancy Networks, BARF, and IDR.