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Monocular Depth Estimation on NYU Depth V2 (AbsRel, delta_1)

4.4AbsRelDepthAnything v2
3.6728.58613.518.414Oct 18, 2022May 19, 2023Dec 19, 2023Jul 20, 2024Feb 19, 2025Sep 21, 2025Apr 23, 2026

Evaluation results

MethodLinks
DepthAnything v2Prior=DINOv2 - 142M, training samples=63M2025.124.497.9
VGGTzero-shot=true2026.044.9996.1
Marigold E2EPrior=SD - 2.3B, training samples=74K2025.125.296.6
Omnizero-shot=true2026.045.4296.22
Marigold v1.1Prior=SD - 2.3B, training samples=74K2025.125.596.4
DA3 giantzero-shot=true2026.045.7994.78
Metric3DPrior=DINOv2 - 142M, training samples=16M2025.125.896.1
MarigoldPrior=SD - 2.3B, training samples=74K2025.125.896.1
VAR-DepthPrior=Switti - 100M, training samples=74K, config=optimized wk2025.126.494.8
DepthFMPrior=SD - 2.3B, training samples=63K2025.126.595.6
Hierarchical Depth NormalizationTraining Data=~ 300K, Zero-shot transfer=true2022.106.994.8
HDNPrior=ImageNet - 14M, training samples=300K2025.126.994.8
Marigoldzero-shot=true2026.047.8192.75
Cut3rzero-shot=true2026.048.2491.64
LeresTraining Data=~ 360K, Zero-shot transfer=true2022.10991.6
LeReSPrior=ImageNet - 1M, training samples=354K2025.12991.6
DPTPrior=ImageNet - 14M, training samples=1.4M2025.129.890.3
MiDaSTraining Data=~ 2M, Zero-shot transfer=true2022.1011.188.5
MiDaSPrior=ImageNet - 1M, training samples=2M2025.1211.188.5
DiverseDepthTraining Data=~ 300K, Zero-shot transfer=true2022.1011.787.5
DiverseDepthPrior=ImageNet - 1M, training samples=320K2025.1211.787.5
Xian et al.Training Data=~ 30K, Zero-shot transfer=true2022.1016.677.2
Chen et al.Training Data=~ 795K, Zero-shot transfer=true2022.1016.677.3
MegaDepthTraining Data=~ 150K, Zero-shot transfer=true2022.1019.471.4
OASISTraining Data=~ 140K, Zero-shot transfer=true2022.1021.966.8
WSVDTraining Data=~ 1.5M, Zero-shot transfer=true2022.1022.665