Monocular Depth Estimation On Nyu Depth V2 4
Métriques
Absolute relative error (AbsRel)
Root mean square error (RMSE)
delta_1
delta_2
delta_3
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Absolute relative error (AbsRel) | Root mean square error (RMSE) | delta_1 | delta_2 | delta_3 | Paper Title | Repository |
|---|---|---|---|---|---|---|---|
| Zhou et al | 0.208 | 0.712 | 67.4 | 90.0 | 96.8 | Moving Indoor: Unsupervised Video Depth Learning in Challenging Environments | - |
| StrutDepth | 0.142 | 0.540 | 81.3 | 95.4 | 98.8 | StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation | - |
| IndoorDepth | 0.126 | 0.494 | 84.5 | 96.5 | 99.1 | Deeper into Self-Supervised Monocular Indoor Depth Estimation | - |
| DistDepth | 0.130 | 0.517 | 83.2 | 96.3 | 99.0 | Toward Practical Monocular Indoor Depth Estimation | - |
| MonoIndoor | 0.134 | 0.526 | 82.3 | 95.8 | 98.9 | MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments | - |
| Zhao et al | 0.189 | 0.686 | 70.1 | 91.2 | 97.8 | Towards Better Generalization: Joint Depth-Pose Learning without PoseNet | - |
| Bian et al | 0.157 | 0.593 | 78.0 | 94.0 | 98.4 | Unsupervised Scale-consistent Depth Learning from Video | - |
| P2Net+PP | 0.147 | 0.553 | 80.4 | 95.2 | 98.7 | P$^{2}$Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation | - |
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