This is a Jittor implementation of PCT: Point Cloud Transformer.
Paper link: https://arxiv.org/pdf/2012.09688.pdf
The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer(PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation and normal estimation tasks
Jittor is a high-performance deep learning framework which is easy to learn and use. It provides interfaces like Pytorch.
You can learn how to use Jittor in following links:
Jittor homepage: https://cg.cs.tsinghua.edu.cn/jittor/
Jittor github: https://github.com/Jittor/jittor
If you has any questions about Jittor or PCT, you can ask in Jittor developer QQ Group: 761222083
Version 1 : https://github.com/uyzhang/PCT_Pytorch (Pytorch version with classification acc 93.2% on ModelNet40)
Version 2 : https://github.com/qq456cvb/Point-Transformers (Pytorch version with classification acc 92.6% on ModelNet40)
Now, we only release the core code of our paper. All code and pretrained models will be available soon.
If it is helpful for your work, please cite this paper:
@misc{guo2020pct,
title={PCT: Point Cloud Transformer},
author={Meng-Hao Guo and Jun-Xiong Cai and Zheng-Ning Liu and Tai-Jiang Mu and Ralph R. Martin and Shi-Min Hu},
year={2020},
eprint={2012.09688},
archivePrefix={arXiv},
primaryClass={cs.CV}
}