Papers › Attention-based Point Cloud Edge Sampling
Attention-based Point Cloud Edge Sampling
Chengzhi Wu, Junwei Zheng, Julius Pfrommer, Jürgen Beyerer
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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94a0da3e2e3fab47 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Part Segmentation | ShapeNet-Part | APES (global_based downsample) | Class Average IoU | 83.7 | #40 of 67 | Archive leaderboard | report |
| 3D Part Segmentation | ShapeNet-Part | APES (global_based downsample) | Instance Average IoU | 85.8 | #40 of 67 | Archive leaderboard | report |
| 3D Part Segmentation | ShapeNet-Part | APES (local_based downsample) | Class Average IoU | 83.1 | #45 of 67 | Archive leaderboard | report |
| 3D Part Segmentation | ShapeNet-Part | APES (local_based downsample) | Instance Average IoU | 85.6 | #45 of 67 | Archive leaderboard | report |
| 3D Point Cloud Classification | ModelNet40 | APES (global-based downsample) | Overall Accuracy | 93.8 | #45 of 111 | Archive leaderboard | report |
| 3D Point Cloud Classification | ModelNet40 | APES (local-based downsample) | Overall Accuracy | 93.5 | #59 of 111 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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