Research map / 3D vision / 3D generation
SPSG: Self-Supervised Photometric Scene Generation From RGB-D Scans
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use 2D view-guided rendering losses instead of 3D target supervision (ablation) | SPSG · Matterport3D | IoU: 0.33 -> 0.39 | Helped |
| use 2D view-guided rendering losses instead of 3D target supervision (ablation) | SPSG · Matterport3D | FID: 80.51 -> 56.03 | Helped |
| add adversarial and perceptual losses to the ℓ1 reconstruction loss (ablation) | SPSG · Matterport3D | FID: 67.92 -> 56.03 | Helped |
| add adversarial loss (ablation) | SPSG · Matterport3D | FID: 62.15 -> 56.03 | Helped |
| add perceptual loss (ablation) | SPSG · Matterport3D | FID: 61.46 -> 56.03 | Helped |
| use CIELAB instead of RGB color space (ablation) | SPSG · Matterport3D | FID: 58.8 -> 56.03 | Helped |
| use CIELAB instead of RGB color space (ablation) | SPSG · Matterport3D | Feature-ℓ1: 0.222 -> 0.219 | Helped |
| use 2D view-guided losses instead of a 3D ℓ1 loss only (ablation) | SPSG · Matterport3D | IoU: 0.31 -> 0.39 | Helped |
| train on real Matterport3D data instead of synthetic data | SG-NN · Matterport3D | IoU: 0.27 -> 0.28 | Helped |
Closest papers by meaning
- SG-NN: Sparse Generative Neural Networks for Self-Supervised Scene Completion of RGB-D Scans (CVPR 2020 · similarity 0.89)
- 3D Gaussian Splatting within Self-Learned Neural View-Dependent Color Fields (NeurIPS 2026 · similarity 0.87)
- Adversarial Texture Optimization From RGB-D Scans (CVPR 2020 · similarity 0.87)
- TransformerFusion: Monocular RGB Scene Reconstruction using Transformers (NeurIPS 2021 · similarity 0.86)
- Coherent 3D Scene Diffusion From a Single RGB Image (NeurIPS 2024 · similarity 0.86)
- ERUPT: Efficient Rendering with Unposed Patch Transformer (CVPR 2025 · similarity 0.85)
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