Methods › Computer Vision › Point Cloud Augmentation › PointAugment
PointAugment
Introduced by Ruihui Li et al. in PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
PointAugment is a an auto-augmentation framework that automatically optimizes and augments point cloud samples to enrich the data diversity when we train a classification network. Different from existing auto-augmentation methods for 2D images, PointAugment is sample-aware and takes an adversarial learning strategy to jointly optimize an augmentor network and a classifier network, such that the augmentor can learn to produce augmented samples that best fit the classifier.
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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PointAugment: an Auto-Augmentation Framework for Point Cloud Classification 25 Feb 2020 · 2 repositories · arXiv:2002.10876Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
Tasks archive 2025-07-28
6 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 Data Augmentation | 1 |
| Classification | 1 |
| Diversity | 1 |
| General Classification | 1 |
| Point Cloud Classification | 1 |
| Retrieval | 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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