Papers › AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry
We introduce a method for learning to generate the surface of 3D shapes. Our approach represents a 3D shape as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape. Beyond its novelty, our new shape generation framework, AtlasNet, comes with significant advantages, such as improved precision and generalization capabilities, and the possibility to generate a shape of arbitrary resolution without memory issues. We demonstrate these benefits and compare to strong baselines on the ShapeNet benchmark for two applications: (i) auto-encoding shapes, and (ii) single-view reconstruction from a still image. We also provide results showing its potential for other applications, such as morphing, parametrization, super-resolution, matching, and co-segmentation.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Shape Reconstruction | Pix3D | AtlasNet | CD | 0.125 | #5 of 5 | Archive leaderboard | report |
| 3D Shape Reconstruction | Pix3D | AtlasNet | EMD | 0.128 | #5 of 5 | Archive leaderboard | report |
| 3D Shape Reconstruction | Pix3D | AtlasNet | IoU | N/A | #5 of 5 | Archive leaderboard | report |
| Point Cloud Completion | Completion3D | AtlasNet | Chamfer Distance | 17.77(?) | #4 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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