3D Point Cloud Data Augmentation

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

5 shown of 5 papers with code (5 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
  28 Jan 2022 6 repositories listed Syntology ran 14 of 29 samples · 15 unverified
  Deep neural networks on 3D point cloud data have been widely used in the real world, especially in safety-critical applications.
• PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
  25 Feb 2020 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  We present PointAugment, a new auto-augmentation framework that automatically optimizes and augments point cloud samples to enrich the data diversity when we train a classification network.
• SageMix: Saliency-Guided Mixup for Point Clouds
  13 Oct 2022 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  Mixup is a simple and widely-used data augmentation technique that has proven effective in alleviating the problems of overfitting and data scarcity.
• On Automatic Data Augmentation for 3D Point Cloud Classification
  11 Dec 2021 1 repository listed
  Data augmentation is an important technique to reduce overfitting and improve learning performance, but existing works on data augmentation for 3D point cloud data are based on heuristics.
• PointMixup: Augmentation for Point Clouds
  14 Aug 2020 1 repository listed
  In this paper, we define data augmentation between point clouds as a shortest path linear interpolation.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.