Methods › Computer Vision › Point Cloud Models › PointASNL
PointASNL
Introduced by Xu Yan et al. in PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
PointASNL is a non-local neural network for point clouds processing It consists of two general modules: adaptive sampling (AS) module and local-Nonlocal (L-NL) module. The AS module first re-weights the neighbors around the initial sampled points from farthest point sampling (FPS), and then adaptively adjusts the sampled points beyond the entire point cloud. The AS module can not only benefit the feature learning of point clouds, but also ease the biased effect of outliers. The L-NL module capture the neighbor and long-range dependencies of the sampled point, and enables the learning process to be insensitive to noise.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling 1 Mar 2020 · 1 repository · arXiv:2003.00492Syntology ran 1 of 6 samples · 5 unverified
Tasks archive 2025-07-28
2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| 3D Point Cloud Classification | 1 |
| Semantic Segmentation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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