Monocular Depth Estimation on NYU Depth Eigen v2 (test)
0.044A.RelEdit2Perc
Evaluation results
| Method | Links | ||||||
|---|---|---|---|---|---|---|---|
| Edit2PercCategory=Generative dense prediction methods2026.07 | 0.044 | — | — | 97.6 | — | — | |
| DAv2Category=Specialized / discriminative methods2026.07 | 0.045 | — | — | 97.9 | — | — | |
| ReChannel-9BCategory=RGB-native field readout, Backbone=FLUX-Klein 9B2026.07 | 0.051 | — | — | 97.4 | — | — | |
| GeoWizardCategory=Generative dense prediction methods2026.07 | 0.052 | — | — | 96.6 | — | — | |
| Lotus-DCategory=Generative dense prediction methods2026.07 | 0.053 | — | — | 97.7 | — | — | |
| MarigoldCategory=Generative dense prediction methods2026.07 | 0.055 | — | — | 96.4 | — | — | |
| GenPerceptCategory=Generative dense prediction methods2026.07 | 0.056 | — | — | 96 | — | — | |
| ReChannel-4BCategory=RGB-native field readout, Backbone=FLUX-Klein 4B2026.07 | 0.056 | — | — | 96.4 | — | — | |
| OursBackbone=U-Net, Parameters=8.6M2022.04 | 0.072 | 0.029 | 0.267 | 95.2 | 98.9 | 99.7 | |
| DeepOptics2022.04 | 0.082 | 0.052 | 0.433 | 93 | 99 | 99.9 | |
| Alhashim et al.2022.04 | 0.093 | 0.05 | 0.382 | 93.2 | 98.9 | 99.7 | |
| PhaseCam3D2022.04 | 0.093 | 0.05 | 0.382 | 93.2 | 98.9 | 99.7 | |
| DepthFormer2022.03 | 0.096 | 0.041 | 0.339 | 92.1 | 98.9 | 99.8 | |
| Long et al.Method category=Other monocular depth estimation2022.04 | 0.101 | 0.044 | 0.377 | 89 | 98.2 | 99.6 | |
| AdaBinsMethod category=Other monocular depth estimation, retrained=true2022.04 | 0.103 | 0.044 | 0.364 | 90.3 | 98.4 | 99.7 | |
| AdaBinsParameters=78M2022.04 | 0.103 | 0.044 | 0.364 | 90.3 | 98.4 | 99.7 | |
| AdaBins2022.03 | 0.103 | 0.044 | 0.364 | 90.3 | 98.4 | 99.7 | |
| P3DepthMethod category=Other monocular depth estimation2022.04 | 0.104 | 0.043 | 0.356 | 89.8 | 98.1 | 99.6 | |
| TransDepth2022.03 | 0.106 | 0.045 | 0.365 | 90 | 98.3 | 99.6 | |
| StruMonoNetMethod category=Plane detection based2022.04 | 0.107 | 0.046 | 0.392 | 88.7 | 98 | 99.5 | |
| Yin et al.Method category=Other monocular depth estimation2022.04 | 0.108 | 0.048 | 0.416 | 87.5 | 97.6 | 99.4 | |
| Huynh et al.Method category=Other monocular depth estimation2022.04 | 0.108 | — | 0.412 | 88.2 | 98 | 99.6 | |
| Yin et al.2022.04 | 0.108 | 0.048 | 0.416 | 87.5 | 97.6 | 99.4 | |
| DAV2022.04 | 0.108 | — | 0.412 | 88.2 | 98 | 99.6 | |
| DAV2022.03 | 0.108 | — | 0.412 | 88.2 | 98 | 99.6 | |
| Lee et al.Method category=Other monocular depth estimation2022.04 | 0.11 | 0.047 | 0.392 | 88.5 | 97.8 | 99.4 | |
| Ranftl et al.Method category=Other monocular depth estimation2022.04 | 0.11 | 0.045 | 0.357 | 90.4 | 98.8 | 99.8 | |
| BTS2022.04 | 0.11 | 0.047 | 0.392 | 88.5 | 97.8 | 99.4 | |
| DPT-HybridPre-training=1.4M samples2022.04 | 0.11 | 0.045 | 0.357 | 90.4 | 98.8 | 99.8 | |
| BTS2022.03 | 0.11 | 0.047 | 0.392 | 88.5 | 97.8 | 99.4 | |
| DPT2022.03 | 0.11 | 0.045 | 0.357 | 90.4 | 98.8 | 99.8 | |
| Fu et al.Method category=Other monocular depth estimation2022.04 | 0.115 | 0.051 | 0.509 | 82.8 | 96.5 | 99.2 | |
| DORN2022.04 | 0.115 | 0.051 | 0.509 | 82.8 | 96.5 | 99.2 | |
| DORN et al.2022.03 | 0.115 | 0.051 | 0.509 | 82.8 | 96.5 | 99.2 | |
| PlaneRCNNMethod category=Plane detection based2022.04 | 0.124 | 0.077 | 0.644 | — | — | — | |
| Laina et al.Method category=Other monocular depth estimation2022.04 | 0.127 | 0.055 | 0.573 | 81.1 | 95.3 | 98.8 | |
| Laina et al.2022.04 | 0.127 | 0.055 | 0.573 | 81.1 | 95.3 | 98.8 | |
| Hao et al.Backbone=ResNet-50, Pre-training=ImageNet2022.04 | 0.127 | 0.053 | 0.555 | 84.1 | 96.6 | 99.1 | |
| Laina et al.2022.03 | 0.127 | 0.055 | 0.573 | 81.1 | 95.3 | 98.8 | |
| Qi et al.2022.04 | 0.128 | 0.057 | 0.569 | 83.4 | 96 | 99 | |
| Yu et al.Method category=Plane detection based2022.04 | 0.134 | 0.057 | 0.503 | 82.7 | 96.3 | 99 | |
| MonoIndoor2022.03 | 0.134 | — | 0.526 | 82.3 | 95.8 | 98.9 | |
| StructDepth2022.03 | 0.14 | 0.06 | 0.534 | 81.7 | 95.5 | 98.8 | |
| PlaneNetMethod category=Plane detection based2022.04 | 0.142 | 0.06 | 0.514 | 81.2 | 95.7 | 98.9 | |
| Li et al. [39]Method category=Other monocular depth estimation2022.04 | 0.143 | 0.063 | 0.635 | 78.8 | 95.8 | 99.1 | |
| P2 NetMethod category=Plane detection based, number of frames=5, self-supervised=true2022.04 | 0.147 | 0.062 | 0.553 | 80.1 | 95.1 | 98.7 | |
| ChakrabartiMethod category=Other monocular depth estimation2022.04 | 0.149 | — | 0.62 | 80.6 | 95.8 | 98.7 | |
| Eigen et al.Method category=Other monocular depth estimation2022.04 | 0.158 | — | 0.641 | 76.9 | 95 | 98.8 | |
| Eigen et al.2022.04 | 0.158 | — | 0.641 | 76.9 | 95 | 98.8 | |
| Eigen et al.2022.03 | 0.158 | — | 0.641 | 76.9 | 95 | 98.8 | |
| Roy et al.Method category=Other monocular depth estimation2022.04 | 0.187 | 0.078 | 0.744 | — | — | — | |
| Liu et al. [45]Method category=Other monocular depth estimation2022.04 | 0.213 | 0.087 | 0.759 | 65 | 90.6 | 97.4 | |
| Wang et al.Method category=Other monocular depth estimation2022.04 | 0.22 | 0.094 | 0.745 | 60.5 | 89 | 97 | |
| Li et al. [38]Method category=Other monocular depth estimation2022.04 | 0.232 | 0.094 | 0.821 | 62.1 | 88.6 | 96.8 | |
| Liu et al. [46]Method category=Other monocular depth estimation2022.04 | 0.335 | 0.127 | 1.06 | — | — | — | |
| Saxena et al.Method category=Other monocular depth estimation2022.04 | 0.349 | — | 1.214 | 44.7 | 74.5 | 89.7 | |
| Karsch et al.Method category=Other monocular depth estimation2022.04 | 0.349 | 0.131 | 1.21 | — | — | — | |
| Ladicky et al.Method category=Other monocular depth estimation2022.04 | — | — | — | 54.2 | 82.9 | 94.1 |