Zhikai Zhang Jian Ding* Li Jiang Dengxin Dai Gui-Song Xia*
Wuhan University KAUST CUHK-Shenzhen Huawei Zurich Research Center

News 🔥

[2024-02] FreePoint is accepted by CVPR 2024. Thanks for the recognition!

TODOs

Release class-agnostic instance segmentation training codebase
Release pretrained checkpoints

Prepare

Please refer to Mask3D for detailed dataset and environment preparation.

Code Structure

We adapt the codebase of Mask3D and Mix3D, which provide a highly modularized framework for 3D Segmentation based on the MinkowskiEngine.

FreePoint
│   ├── main_instance_segmentation_freepoint.py <- the main file
│   ├── conf                          <- hydra configuration files
│   ├── datasets
│   │   ├── preprocessing             <- folder with preprocessing scripts
│   │   ├── semseg_freepoint.py                 <- indoor dataset
│   │   └── utils_freepoint.py
│   ├── models                        <- Mask3D modules
│   ├── trainer
│   │   ├── __init__.py
│   │   └── trainer.py                <- train loop
│   └── utils
├── data
│   ├── processed                     <- folder for preprocessed datasets
│   └── raw                           <- folder for raw datasets
├── scripts                           <- train scripts
├── docs
├── README.md
└── saved                             <- folder that stores models and logs

Dependencies:

The main dependencies of the project are the following:

python: 3.10.9
cuda: 11.3

BibTex

If you find this repository helpful, please cite our work:

@inproceedings{zhang2024freepoint,
  title={Freepoint: Unsupervised point cloud instance segmentation},
  author={Zhang, Zhikai and Ding, Jian and Jiang, Li and Dai, Dengxin and Xia, Guisong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={28254--28263},
  year={2024}
}