Research map / 3D vision / Depth and stereo
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add motion consistency loss L_c (ablation) | Dynamo-Depth · Waymo Open | Abs Rel (all): 0.525 -> 0.116 | Helped |
| add motion initialization (ablation) | Dynamo-Depth · Waymo Open | Abs Rel (all): 0.522 -> 0.116 | Helped |
| add above-ground loss L_g (ablation) | Dynamo-Depth · Waymo Open | Abs Rel (all): 0.266 -> 0.116 | Helped |
| add above-ground loss L_g (ablation) | Dynamo-Depth · Waymo Open moving objects | Abs Rel: 0.211 -> 0.194 | No clear effect |
| add ImageNet pretraining of encoder (ablation) | Dynamo-Depth · Waymo Open | Abs Rel (all): 0.136 -> 0.116 | Helped |
| model independent motion (Dynamo-Depth vs LiteMono) | LiteMono depth network · Waymo Open moving objects | Abs Rel: 0.599 -> 0.194 | Helped |
| model independent motion (Dynamo-Depth vs LiteMono) | LiteMono depth network · nuScenes | Abs Rel (all): 0.419 -> 0.179 | Helped |
| model independent motion (Dynamo-Depth vs LiteMono) | LiteMono depth network · KITTI Eigen | Abs Rel: 0.101 -> 0.112 | Hurt |
Closest papers by meaning
- Mining Supervision for Dynamic Regions in Self-Supervised Monocular Depth Estimation (CVPR 2024 · similarity 0.88)
- Seurat: From Moving Points to Depth (CVPR 2025 · similarity 0.88)
- Depth2Pose: A Pose-Based Benchmark for Monocular Depth Estimation without Ground-Truth Depth (NeurIPS 2026 · similarity 0.87)
- Stereo4D: Learning How Things Move in 3D from Internet Stereo Videos (CVPR 2025 · similarity 0.86)
- Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos (CVPR 2025 · similarity 0.86)
- Video Depth Anything: Consistent Depth Estimation for Super-Long Videos (CVPR 2025 · similarity 0.86)
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