This is the official implementation of [PointMCD] (TMM 2023), which is designed for boosting deep 3D point cloud encoders by distilling discriminative cross-modal visual knowledge extracted from multi-view rendered images for a variety of 3D shape analysis and recognition applications.

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.

[Datasets] Download our pre-processed datasets and put them under the data folder. Or you can also use our pre-processing code to render multi-view images and the corresponding point-wise visibility for your own data.

[Scripts] We provided the training scripts of different teacher networks and released their corresponding pre-trained model parameters under the ckpt/teacher folder. The teacher knowledge information is exported in advance, which can be downloaded from here, and then put under the expt folder. The trained student models will be stored under the ckpt/student folder.

As a universal plug-in component for generic deep set architectures, one can easily integrate our approach to various types of deep point cloud encoders, such as PointNet++ and CurveNet, as experimented in our paper. Your further efforts in applying PointMCD to other more powerful point cloud backbones and richer downstream task scenarios are warmly welcomed.

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

@article{zhang2023pointmcd,
  title={PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognition},
  author={Zhang, Qijian and Hou, Junhui and Qian, Yue},
  journal={IEEE Transactions on Multimedia},
  year={2023}
}