Generating 3D Point Clouds

Benchmarks archive 2025-07-28

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Libraries

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Datasets archive 2025-07-28

1 dataset 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

6 shown of 6 papers with code (11 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.

• Adversarial Autoencoders for Compact Representations of 3D Point Clouds
  19 Nov 2018 4 repositories listed
  Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds.
• Geometric Algebra Attention Networks for Small Point Clouds
  5 Oct 2021 2 repositories listed Syntology ran 3 of 3 samples · 0 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…
• Hypernetwork approach to generating point clouds
  10 Feb 2020 2 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)
  The main idea of our HyperCloud method is to build a hyper network that returns weights of a particular neural network (target network) trained to map points from a uniform unit ball distribution into a 3D shape.
• Point-E: A System for Generating 3D Point Clouds from Complex Prompts
  16 Dec 2022 1 repository listed Syntology ran 5 of 11 samples · 6 unverified
  This is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes.
• Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds
  13 Dec 2019 1 repository listed
  Constructing high-quality generative models for 3D shapes is a fundamental task in computer vision with diverse applications in geometry processing, engineering, and design.
• PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention
  12 Oct 2018 1 repository listed
  Generating 3D point clouds is challenging yet highly desired.

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 2026-09-25.