Research map / 3D vision / 3D generation
Unsupervised Learning of 3D Structure from Images
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase number of generation steps (more recurrent steps T) (ablation) | volumetric generative model · Primitives, MNIST3D, ShapeNet | log-likelihood bound | Helped |
| add context views (1 to 3) to conditioning (ablation) | volumetric generative model · Primitives, MNIST3D, ShapeNet | log-likelihood bound | Helped |
| replace generative model with deterministic 6-layer volumetric conv net (3 views) | volumetric baseline · Primitives, MNIST3D, ShapeNet | log-likelihood bound | Hurt |
| train with 3D volumetric data vs. 2D multi-view images with learned neural projection | volumetric model · multi-view depth images | qualitative reconstruction | No clear effect |
Closest papers by meaning
- Learning Unsupervised Hierarchical Part Decomposition of 3D Objects From a Single RGB Image (CVPR 2020 · similarity 0.90)
- Unsupervised Causal Generative Understanding of Images (NeurIPS 2022 · similarity 0.90)
- Unsupervised 3D Structure Inference from Category-Specific Image Collections (CVPR 2024 · similarity 0.90)
- 3DP3: 3D Scene Perception via Probabilistic Programming (NeurIPS 2021 · similarity 0.89)
- Large Spatial Model: End-to-end Unposed Images to Semantic 3D (NeurIPS 2024 · similarity 0.89)
- Learning Partonomic 3D Reconstruction from Image Collections (CVPR 2025 · similarity 0.89)
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