Attention-Based Point Cloud Edge Sampling

Chengzhi Wu, Junwei Zheng, Julius Pfrommer, Jürgen Beyerer

CVPR 2023 pp. 5333-5343

doi:10.1109/CVPR52729.2023.00516 /cvpr/2023/wu2023cvpr-attentionbased/

Abstract

Point cloud sampling is a less explored research topic for this data representation. The most commonly used sampling methods are still classical random sampling and farthest point sampling. With the development of neural networks, various methods have been proposed to sample point clouds in a task-based learning manner. However, these methods are mostly generative-based, rather than selecting points directly using mathematical statistics. Inspired by the Canny edge detection algorithm for images and with the help of the attention mechanism, this paper proposes a non-generative Attention-based Point cloud Edge Sampling method (APES), which captures salient points in the point cloud outline. Both qualitative and quantitative experimental results show the superior performance of our sampling method on common benchmark tasks.

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Cite

Text

Wu et al. "Attention-Based Point Cloud Edge Sampling." Conference on Computer Vision and Pattern Recognition, 2023. doi:10.1109/CVPR52729.2023.00516

Markdown

[Wu et al. "Attention-Based Point Cloud Edge Sampling." Conference on Computer Vision and Pattern Recognition, 2023.](https://mlanthology.org/cvpr/2023/wu2023cvpr-attentionbased/) doi:10.1109/CVPR52729.2023.00516

BibTeX

@inproceedings{wu2023cvpr-attentionbased,
  title     = {{Attention-Based Point Cloud Edge Sampling}},
  author    = {Wu, Chengzhi and Zheng, Junwei and Pfrommer, Julius and Beyerer, Jürgen},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2023},
  pages     = {5333-5343},
  doi       = {10.1109/CVPR52729.2023.00516},
  url       = {https://mlanthology.org/cvpr/2023/wu2023cvpr-attentionbased/}
}