Research map / 3D vision / NeRF and radiance fields
Generative Occupancy Fields for 3D Surface-Aware Image Synthesis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| remove normal regularization L_normal (ablation) | GOF · BFM | weighted depth variance: 2.54 -> 2.66 | Helped |
| remove opacity regularization L_opacity (ablation) | GOF · CelebA | Inception Score: 2.87 -> 2.91 | No clear effect |
| remove opacity regularization L_opacity (ablation) | GOF · Cats | weighted depth variance: 4.28 -> 4.42 | Helped |
| remove shrinking sampling process (ablation) | GOF · BFM | normal map quality | Helped |
| replace pi-GAN cumulative rendering with GOF occupancy transition | pi-GAN · BFM | MAD: 20.46 -> 13.81 | Helped |
| replace pi-GAN cumulative rendering with GOF occupancy transition | pi-GAN · BFM | SIDE: 0.727 -> 0.779 | No clear effect |
| replace pi-GAN cumulative rendering with GOF occupancy transition | pi-GAN · CelebA | depth variance: 14.58 -> 4.58 | Helped |
| replace pi-GAN cumulative rendering with GOF occupancy transition | pi-GAN · CelebA | mean curvature: 25.94 -> 23.13 | Helped |
| render with surface point only instead of multiple points | GOF · 256x256 CPU rendering | time per image: 78s -> 56s | Helped |
| apply GOF on CARLA | pi-GAN · CARLA | FID: 29.6 -> 29.3 | No clear effect |
Closest papers by meaning
- GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis (NeurIPS 2020 · similarity 0.91)
- GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation (CVPR 2022 · similarity 0.89)
- SIGNeRF: Scene Integrated Generation for Neural Radiance Fields (CVPR 2024 · similarity 0.88)
- GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling (NeurIPS 2024 · similarity 0.88)
- Learning Robust Generalizable Radiance Field with Visibility and Feature Augmented Point Representation (ICLR 2024 · similarity 0.87)
- ODG: Occupancy Prediction Using Dual Gaussians (NeurIPS 2025 · similarity 0.87)
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