Research map / 3D vision / Depth and stereo
RayNet: Learning Volumetric 3D Reconstruction With Ray Potentials
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add MRF inference with ray potentials to the CNN frontend (ablation) | RayNet · Aerial dataset | accuracy (mean): 0.0804 -> 0.0611 | Helped |
| add MRF inference with ray potentials to the CNN frontend (ablation) | RayNet · Aerial dataset | mean depth error (mean): 0.0977 -> 0.0744 | Helped |
| add MRF inference with ray potentials to the CNN frontend (ablation) | RayNet · Aerial dataset | Chamfer distance: 0.0479 -> 0.0368 | Helped |
| use RayNet instead of the strongest listed baseline | RayNet · BUDDHA | accuracy (mean): 0.637 -> 1.993 | Hurt |
| use RayNet instead of the strongest listed baseline | RayNet · BUDDHA | completeness (mean): 0.677 -> 0.481 | Helped |
| use RayNet instead of the strongest listed baseline | RayNet · BIRD | accuracy (mean): 1.493 -> 2.618 | Hurt |
| use RayNet instead of the strongest listed baseline | RayNet · BIRD | completeness (mean): 1.612 -> 0.983 | Helped |
Closest papers by meaning
- RayMVSNet: Learning Ray-Based 1D Implicit Fields for Accurate Multi-View Stereo (CVPR 2022 · similarity 0.90)
- RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency (NeurIPS 2023 · similarity 0.90)
- CRAYM: Neural Field Optimization via Camera RAY Matching (NeurIPS 2024 · similarity 0.88)
- RaySt3R: Predicting Novel Depth Maps for Zero-Shot Object Completion (NeurIPS 2025 · similarity 0.87)
- Zero-Shot Novel View and Depth Synthesis with Multi-View Geometric Diffusion (CVPR 2025 · similarity 0.87)
- DERD-Net: Learning Depth from Event-based Ray Densities (NeurIPS 2025 · similarity 0.86)
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