Monocular Depth Estimation on KITTI improved ground truth (Eigen split)

0.052Abs RelDIFFNet + SfM-TTR

Evaluation results

Method
DIFFNet + SfM-TTR2022.11	0.052	0.229	2.444	0.085	97.3	99.4	99.8
SQLdepth (ResNet-50, 37M)2023.09	0.052	0.223	2.55	0.084	97.1	99.5	99.8
ManyDepth + Ph-TTR2022.11	0.053	0.252	2.774	0.089	96.2	99.3	99.8
DIFFNet + Ph-TTR2022.11	0.053	0.252	2.778	0.09	96.5	99.3	99.8
AdaBins + SfM-TTR2022.11	0.054	0.138	1.885	0.078	97.8	99.6	99.9
ManyDepth + SfM-TTR2022.11	0.054	0.252	2.51	0.089	96.6	99.2	99.8
SQLdepth (ResNet-50, 31M)2023.09	0.054	0.276	2.819	0.092	96.4	99.3	99.8
ManyDepth (ResNet-50, 37M)2023.09	0.055	0.305	2.945	0.094	96.3	99.2	99.7
ManyDepth + Ph-TTR2022.11	0.056	0.322	3.034	0.096	96.1	99.2	99.7
DIFFNet + SfM-TTR2022.11	0.056	0.273	2.6	0.093	96.9	99.2	99.7
ManyDepth + SfM-TTR2022.11	0.057	0.294	2.648	0.094	96.3	99	99.7
DIFFNet + Ph-TTR2022.11	0.057	0.285	2.9	0.095	96.1	99.2	99.8
AdaBins2022.11	0.058	0.19	2.36	0.088	96.4	99.5	99.9
ManyDepth (MR, 36M)2023.09	0.058	0.334	3.137	0.101	95.8	99.1	99.7
SQLdepth (Efficient-b5, 37M)2023.09	0.058	0.287	3.039	0.096	95.9	99.2	99.8
SQLdepth (ResNet-50, 37M)2023.09	0.058	0.289	2.925	0.095	96.2	99.3	99.8
ManyDepth2022.11	0.059	0.297	2.96	0.097	95.4	99.1	99.8
SCIPAD2024.07	0.059	0.287	2.871	0.095	96.4	99.3	99.8
CADepth + SfM-TTR2022.11	0.06	0.263	2.62	0.096	96.2	99.2	99.7
AdaBins + SfM-TTR2022.11	0.06	0.204	2.26	0.091	97	99.3	99.8
SQLdepth (ResNet-50, 31M)2023.09	0.061	0.317	3.055	0.1	95.7	99.2	99.7
SQLdepth2024.07	0.061	0.317	3.055	0.1	95.7	99.2	99.7
ManyDepth2022.11	0.064	0.345	3.116	0.103	94.9	98.9	99.7
DIFFNet2022.11	0.066	0.318	3.078	0.103	95.3	99.2	99.8
SQLdepth (Efficient-b5, 34M)2023.09	0.066	0.356	3.344	0.107	94.7	98.9	99.7
MonoViT2022.08	0.067	0.328	3.108	0.104	95	99.2	99.8
CADepth + SfM-TTR2022.11	0.068	0.328	2.821	0.106	95.5	99	99.6
DynamicDepth2023.09	0.068	0.362	3.454	0.111	94.3	99.1	99.8
DynamicDepth2024.07	0.068	0.362	3.454	0.111	94.3	99.1	99.8
TriDepth2024.07	0.068	0.359	3.341	0.11	94.4	98.9	99.7
CADepth-Net2023.09	0.07	0.346	3.168	0.109	94.5	99.1	99.7
ManyDepth2024.07	0.07	0.399	3.455	0.113	94.1	98.9	99.7
PackNet+2022.08	0.071	0.359	3.153	0.109	94.4	99	99.7
DIFFNet2022.11	0.071	0.361	3.23	0.11	94.6	99	99.7
AdaBins2022.11	0.072	0.325	3.134	0.112	94.1	99	99.8
CADepth2022.11	0.073	0.359	3.287	0.112	94.1	99	99.7
MonoViT2022.08	0.075	0.389	3.419	0.115	93.8	98.9	99.7
DIFFNet2022.08	0.076	0.412	3.494	0.119	93.5	98.8	99.6
CADepth2022.11	0.078	0.403	3.432	0.119	93.3	98.8	99.7
PackNet-SfM2023.09	0.078	0.42	3.485	0.121	93.1	98.6	99.6
PackNet-SfM2024.07	0.078	0.42	3.485	0.121	93.1	98.6	99.6
CADepth2022.08	0.08	0.45	3.649	0.124	92.7	98.6	99.6
Monodepth2 (34M)2023.09	0.08	0.466	3.681	0.127	92.6	98.5	99.5
CADepth-Net2023.09	0.08	0.442	3.639	0.124	92.7	98.6	99.6
Monodepth22024.07	0.08	0.466	3.681	0.127	92.6	98.5	99.5
Johnston et al.2022.08	0.081	0.484	3.716	0.126	92.7	98.5	99.6
Johnston et al.2023.09	0.081	0.484	3.716	0.126	92.7	98.5	99.6
CADepth + Ph-TTR2022.11	0.082	0.426	3.565	0.124	92.3	98.6	99.7
Wang et al.2023.09	0.082	0.462	3.739	0.127	92.3	98.4	99.6
HR-Depth2022.08	0.085	0.471	3.769	0.13	91.9	98.5	99.6
CADepth + Ph-TTR2022.11	0.088	0.475	3.723	0.132	91.4	98.4	99.6
Monodepth22022.08	0.09	0.545	3.942	0.137	91.4	98.3	99.5
Monodepth2 (34M)2023.09	0.091	0.531	3.742	0.135	91.6	98.4	99.5
MonoPP2025.12	0.107	0.835	4.658	0.186	89.1	96.2	98.2
GfM2025.12	0.107	0.808	4.918	0.194	87	95.7	98.1
FUMET2025.12	0.108	0.785	4.736	0.195	87.1	95.8	98.1
VADepth2025.12	0.109	0.785	4.624	0.19	87.5	96	98.2
DynaDepth2025.12	0.109	0.787	4.705	0.195	86.9	95.8	98.1
PackNet2025.12	0.111	0.829	4.788	0.199	86.4	95.4	98
GroCo2025.12	0.113	0.851	4.756	0.197	87	95.8	98
DNet2025.12	0.118	0.925	4.918	0.199	86.2	95.3	97.9
Wagstaff et al.2025.12	0.123	0.996	5.253	0.213	84	94.7	97.8
Monodepth22025.12	0.126	0.973	4.88	0.198	86.4	95.7	98
Sui et al.2025.12	0.128	0.936	5.063	0.214	84.7	95.1	97.8
Wagstaff et al.2025.12	0.155	1.657	5.615	0.236	80.9	92.4	95.9