Research map / 3D vision / Depth and stereo
Trust Your Model: Light Field Depth Estimation With Inline Occlusion Handling
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Handle occlusions inline using the current depth model instead of pre-filtering potentially occluded pixels | OBER-cross+ANP · Figure 1 scene | Bad pixels (error > 0.07 px): 15.5 -> 10.8 | Helped |
Closest papers by meaning
- Robust Light Field Depth Estimation for Noisy Scene With Occlusion (CVPR 2016 · similarity 0.88)
- Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fields (CVPR 2020 · similarity 0.88)
- Towards Multimodal Depth Estimation From Light Fields (CVPR 2022 · similarity 0.87)
- Accurate Depth and Normal Maps From Occlusion-Aware Focal Stack Symmetry (CVPR 2017 · similarity 0.87)
- PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation (CVPR 2024 · similarity 0.85)
- Blurry-Edges: Photon-Limited Depth Estimation from Defocused Boundaries (CVPR 2025 · similarity 0.84)
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