3D Surface Generation

Description from the archive archive 2025-07-28.

Benchmarks 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 (10 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.

• AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
  15 Feb 2018 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  We introduce a method for learning to generate the surface of 3D shapes.
• DoubleDiffusion: Combining Heat Diffusion with Denoising Diffusion for Texture Generation on 3D Meshes
  6 Jan 2025 1 repository listed
  This paper addresses the problem of generating textures for 3D mesh assets.
• Towards Automating the Retrospective Generation of BIM Models: A Unified Framework for 3D Semantic Reconstruction of the Built Environment
  3 Jun 2024 1 repository listed
  The adoption of Building Information Modeling (BIM) is beneficial in construction projects.
• Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility
  18 Aug 2021 1 repository listed
  Then the visibility information of multiple views is aggregated to generate a 3D mesh model by solving an optimization problem considering visibility in which a novel adaptive visibility weighting in surface…
• GAMesh: Guided and Augmented Meshing for Deep Point Networks
  19 Oct 2020 1 repository listed
  We present a new meshing algorithm called guided and augmented meshing, GAMesh, which uses a mesh prior to generate a surface for the output points of a point network.
• 3D-CODED : 3D Correspondences by Deep Deformation
  13 Jun 2018 1 repository listed
  By predicting this feature for a new shape, we implicitly predict correspondences between this shape and the template.

Syntology lines on 1 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.