Boosting Point Clouds Rendering via Radiance Mapping

This is the official code of AAAI'23 paper Boosting Point Clouds Rendering via Radiance Mapping written in PyTorch.

Paper

Installation

conda create -n bpcr python=3.8
conda activate bpcr

conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=10.2 -c pytorch
pip install matplotlib
pip install opencv-python
pip install lpips
pip install piqa==1.1.8
pip install tensorboard
pip install ConfigArgParse
pip install open3d

python setup.py install

Data Preparation

The layout should look like this

code
├── data
    ├── nerf_synthetic
    ├── dtu
    |   ├── dtu_110
    │   │   │── cams_1
    │   │   │── image
    │   │   │── mask
    │   │   │── npbgpp.ply
    |   ├── dtu_114
    |   ├── dtu_118
    ├── scannet
    │   │   │──0000
    |   │   │   │──color_select
    |   │   │   │──pose_select
    |   │   │   |──intrinsic
    |   │   │   |──00.ply
    │   │   │──0043
    │   │   │──0045
    ├── pc
    |   ├── nerf
    │   │   │── chair.ply
    │   │   │── drums.ply  

NeRF-Synthetic: Please download dataset from NeRF and put the unpacked files in ./data/nerf_synthetic. To generate point clouds, run Point-NeRF and save the point clouds in ./data/pc/nerf. You can also download point clouds from here.

DTU: Please download images and masks from IDR and camera parameters from PatchmatchNet. We use the point clouds provided by npbg++.

ScanNet: Please download data from ScanNet and run select_scan.py to select the frames. We use the point cloud provided by ScanNet for scene0000_00 and point clouds provided by npbg++ for two other scenes. For scene0043_00, the frames after 1000 are ignored because the camera parameters are -inf.

Rasterization

python run_rasterize.py --config=configs/chair.txt

Please change the config file to run other scenes. The fragments would be saved in ./data/fragments.

Training

python main.py --config=configs/chair.txt

Before training, please ensure that the fragments of this scene already exist. The results would be saved in ./logs. You can also run tensorboard to observe training and testing

tensorboard --logdir=logs

Acknowledgements and Citation

The code in rasterization borrows a lot from Pytorch3D.

If you find this project useful in your research, please cite the following papers:

Huang X, Zhang Y, Ni B, et al. Boosting point clouds rendering via radiance mapping[C]//Proceedings of the AAAI conference on artificial intelligence. 2023, 37(1): 953-961.

or in bibtex:

@inproceedings{huang2023boosting,
  title={Boosting point clouds rendering via radiance mapping},
  author={Huang, Xiaoyang and Zhang, Yi and Ni, Bingbing and Li, Teng and Chen, Kai and Zhang, Wenjun},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={37},
  number={1},
  pages={953--961},
  year={2023}
}