Depth Estimation On Nyu Depth V2
Metrics
RMS
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | RMS |
|---|---|
| deep-optics-for-monocular-depth-estimation | 0.433 |
| p3depth-monocular-depth-estimation-with-a | 0.356 |
| pad-net-multi-tasks-guided-prediction-and | 0.792 |
| evp-enhanced-visual-perception-using-inverse | 0.224 |
| deep-optics-for-monocular-depth-estimation | 0.4325 |
| focus-on-defocus-bridging-the-synthetic-to | - |
| transformers-solve-the-limited-receptive | 0.365 |
| dinov2-learning-robust-visual-features | 0.279 |
| adabins-depth-estimation-using-adaptive-bins | 0.364 |
| enforcing-geometric-constraints-of-virtual | 0.416 |
| revealing-the-dark-secrets-of-masked-image | 0.304 |
| 3d-ken-burns-effect-from-a-single-image | 0.30 |
| deep-ordinal-regression-network-for-monocular | 0.509 |
| revealing-the-dark-secrets-of-masked-image | 0.287 |
| a2j-anchor-to-joint-regression-network-for-3d | - |
| multi-scale-continuous-crfs-as-sequential | 0.586 |
| from-big-to-small-multi-scale-local-planar | 0.407 |