Research map / 3D vision / Depth and stereo
Just Look at the Image: Viewpoint-Specific Surface Normal Prediction for Improved Multi-View Reconstruction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add view-specific CNN normal prediction and integrate predicted normals into depth maps | MVS initialization · DTU, 14 objects | completeness: 14 % | Helped |
| Add view-specific CNN normal prediction and integrate predicted normals into depth maps | MVS initialization · DTU, 14 objects | overall quality: 11% | Helped |
| Predict missing normals with a CNN trained on the same image's MVS normals | MVS normal maps · DTU, 14 objects | mean angular error: 11◦ -> 18◦ | Hurt |
| Predict missing normals with a CNN trained on the same image's MVS normals | MVS normal maps · DTU, 14 objects | mean-of-median angular error: 9◦ -> 16◦ | Hurt |
| Train one normal predictor for multiple views instead of an individual model per image (ablation) | View-specific normal prediction · DTU scenes | normal prediction performance | Hurt |
| Use a per-image model rather than one model for multiple views (ablation) | Normal prediction · DTU scenes | normal prediction performance | Helped |
Closest papers by meaning
- Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance (NeurIPS 2020 · similarity 0.91)
- Normal Assisted Stereo Depth Estimation (CVPR 2020 · similarity 0.90)
- Uncertainty-Aware Deep Multi-View Photometric Stereo (CVPR 2022 · similarity 0.90)
- Marr Revisited: 2D-3D Alignment via Surface Normal Prediction (CVPR 2016 · similarity 0.89)
- PMNI: Pose-free Multi-view Normal Integration for Reflective and Textureless Surface Reconstruction (CVPR 2025 · similarity 0.89)
- SuperNormal: Neural Surface Reconstruction via Multi-View Normal Integration (CVPR 2024 · similarity 0.87)
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