Research map / 3D vision / 3D generation
Neural Scene De-Rendering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add segment proposals instead of box proposals (ablation) | NSD · Abstract Scene | reconstruction error (%): 28.12 -> 23.76 | Helped |
| Add segment proposals instead of box proposals (ablation) | NSD · Minecraft | reconstruction error (%): 11.42 -> 7.71 | Helped |
| Add segment proposals instead of box proposals (ablation) | NSD · Abstract Scene | inference error (%): 47.85 -> 44.19 | Helped |
| Add segment proposals instead of box proposals (ablation) | NSD · Minecraft | inference error (%): 32.20 -> 32.11 | No clear effect |
| Add REINFORCE to segment-proposal model (ablation) | NSD · Abstract Scene | reconstruction error (%): 23.76 -> 22.44 | Helped |
| Add REINFORCE to segment-proposal model (ablation) | NSD · Minecraft | reconstruction error (%): 7.71 -> 5.73 | Helped |
| Add REINFORCE to segment-proposal model (ablation) | NSD · Abstract Scene | inference error (%): 44.19 -> 45.09 | Hurt |
| Add REINFORCE to segment-proposal model (ablation) | NSD · Minecraft | inference error (%): 32.11 -> 28.79 | Helped |
| Add analysis-by-synthesis refinement to the REINFORCE model (ablation) | NSD · Abstract Scene | reconstruction error (%): 22.44 -> 21.55 | Helped |
| Add analysis-by-synthesis refinement to the REINFORCE model (ablation) | NSD · Minecraft | reconstruction error (%): 5.73 -> 5.05 | Helped |
| Use semi-supervised training with labels on 10% of training images, then unlabeled reconstruction fine-tuning (ablation) | NSD · Abstract Scene | reconstruction error (%): 21.55 vs 21.96 | No clear effect |
| Use semi-supervised training with labels on 10% of training images, then unlabeled reconstruction fine-tuning (ablation) | NSD · Minecraft | reconstruction error (%): 5.05 vs 7.62 | Hurt |
Closest papers by meaning
- 3D-Aware Scene Manipulation via Inverse Graphics (NeurIPS 2018 · similarity 0.90)
- DORSal: Diffusion for Object-centric Representations of Scenes $\textit{et al.}$ (ICLR 2024 · similarity 0.86)
- The Scene Language: Representing Scenes with Programs, Words, and Embeddings (CVPR 2025 · similarity 0.86)
- Compositional Scene Understanding through Inverse Generative Modeling (ICML 2025 · similarity 0.85)
- SCION: Scene Composition via Instanced Neural Primitives (NeurIPS 2026 · similarity 0.84)
- DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features (NeurIPS 2024 · similarity 0.84)
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