Classify 3D Point Clouds

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

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

Subtasks archive 2025-07-28

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Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

4 shown of 4 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.

• Geometric Algebra Attention Networks for Small Point Clouds
  5 Oct 2021 2 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  Much of the success of deep learning is drawn from building architectures that properly respect underlying symmetry and structure in the data on which they operate - a set of considerations that have been united under…
• AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds
  1 Dec 2019 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  Our proposed attack increases the attack success rate by up to 40% for those transferred to unseen networks (transferability), while maintaining a high success rate on the attacked network.
• Point Convolutional Neural Networks by Extension Operators
  27 Mar 2018 1 repository listed
  This paper presents Point Convolutional Neural Networks (PCNN): a novel framework for applying convolutional neural networks to point clouds.
• Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)
  20 Mar 2018 1 repository listed
  Traditional convolution layers are specifically designed to exploit the natural data representation of images -- a fixed and regular grid.

Syntology lines on 2 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.