This repository contains PyTorch implementation of WyPR, as described in the paper 3D Spatial Recognition without Spatially Labeled 3D, Ren et al., CVPR 2021.

WyPR is a Weakly-supervised framework for Point cloud Recognition, requiring only scene-level class tags as supervision. WyPR jointly addresses three core 3D recognition tasks: point-level semantic segmentation, 3D proposal generation, and 3D object detection, coupling their predictions through self and cross-task consistency losses. In conjunction with standard multiple-instance learning (MIL) and self-training objectives, WyPR can detect and segment objects in point cloud without access to any spatial labels at training time. WyPR is evaluated on ScanNet and S3DIS datasets, and outperforms prior state of the art weakly-supervised works by a great margin.

Please follow the instructions in docs/INSTALL.md.

Please follow the wypr/dataset/*/README.md for downloading and pre-processing datasets.

Please check docs/RUNNING.md for detailed running instructions and pre-trained models.

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

@inproceedings{ren2021wypr,
  title = {3D Spatial Recognition without Spatially Labeled 3D},
  author = {Ren, Zhongzheng and Misra, Ishan and Schwing, Alexander G. and Girdhar, Rohit},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021}
}

See the LICENSE for more details.

For help or issues using WyPR, please submit a GitHub issue or a PR request.

Before you do this, make sure you have checked CODE_OF_CONDUCT, CONTRIBUTING, ISSUE_TEMPLATE, and PR_TEMPLATE.

We thank the authors for releasing Pointnet2_PyTorch, VoteNet, PointContrast, MinkowskiEngine, and detectron2.