Unsupervised 3D Point Cloud Linear Evaluation

Description from the archive archive 2025-07-28.

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Most implemented papers archive 2025-07-28

6 shown of 6 papers with code (8 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.

• SO-Net: Self-Organizing Network for Point Cloud Analysis
  12 Mar 2018 3 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds.
• FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
  19 Dec 2017 3 repositories listed
  Recent deep networks that directly handle points in a point set, e.
• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
  24 Oct 2016 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  We study the problem of 3D object generation.
• Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds
  1 Sep 2021 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immense variations introduced by…
• Unsupervised Point Cloud Pre-Training via Occlusion Completion
  2 Oct 2020 1 repository listed
  We find that even when we construct a single pre-training dataset (from ModelNet40), this pre-training method improves accuracy across different datasets and encoders, on a wide range of downstream tasks.
• Self-supervised Learning of Point Clouds via Orientation Estimation
  1 Aug 2020 1 repository listed
  A point cloud can be rotated in infinitely many ways, which provides a rich label-free source for self-supervision.

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.