This is the official implementation of [RegGeoNet] (IJCV 2022), an unsupervised neural architecture to parameterize an unstructured 3D point cloud into a regular 2D image representation structure called deep geometry image (DeepGI), such that spatial coordinates of unordered 3D points are encoded in three-channel grid pixels.

This code has been tested with Python 3.9, PyTorch 1.10.1, CUDA 11.1 and cuDNN 8.0.5 on Ubuntu 20.04.

• Install the differentiable computer vision library Kornia

pip install git+https://github.com/kornia/kornia

Run para_pc.py under the scripts/para/ folder to convert a given 3D point cloud into its DeepGI representation.

Different downstream tasks can be performed directly on the generated DeepGIs, as an equivalent way of processing point cloud data. The pre-processed DeepGI-format datasets can be downloaded here. Please put them under the data folder. Our pre-trained models are provided under the ckpt folder.

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

@article{zhang2022reggeonet,
  title={RegGeoNet: Learning Regular Representations for Large-Scale 3D Point Clouds},
  author={Zhang, Qijian and Hou, Junhui and Qian, Yue and Chan, Antoni B and Zhang, Juyong and He, Ying},
  journal={International Journal of Computer Vision},
  volume={130},
  number={12},
  pages={3100--3122},
  year={2022}
}