Depth Prediction on Cityscapes (test)

5.443RMSEPilzer et al. (Row 1)

Evaluation Results

Method
Pilzer et al. (Row 1)2019.06	5.443	0.44	6.036	0.398	0.73	0.887	0.944
SwinMTL2024.03	5.481	0.089	1.051	0.139	0.921	0.976	0.99
Pilzer et al. (Row 2)2019.06	5.741	0.467	7.399	0.493	0.735	0.89	0.945
Pilzer et al.2024.03	5.745	0.438	5.713	0.4	0.711	0.877	0.94
DynamicDepth2022.03	5.867	0.103	1	0.157	0.895	0.974	0.991
DynamicDepth2024.03	5.867	0.103	1	0.157	0.895	0.974	0.991
ManyDepth2021.04	6.223	0.114	1.193	0.17	0.875	0.967	0.989
ManyDepth2022.03	6.223	0.114	1.193	0.17	0.875	0.967	0.989
SwinMTL2024.03	6.352	—	—	—	—	—	—
Insta-DM2021.02	6.437	0.111	1.158	0.182	0.868	0.961	0.983
InstaDM2022.03	6.437	0.111	1.158	0.182	0.868	0.961	0.983
GLNet2024.03	6.437	0.111	—	0.182	0.868	0.961	0.983
InstaDM2024.03	6.437	0.111	1.158	0.182	0.868	0.961	0.983
3-ways2024.03	6.528	—	—	—	—	—	—
[44]2024.03	6.649	—	—	—	—	—	—
PanopticDepth2022.06	6.69	—	—	—	—	—	—
Lee et al.2022.03	6.695	0.116	1.213	0.186	0.852	0.951	0.982
Lee et al.2024.03	6.695	0.116	1.213	0.186	0.852	0.951	0.982
PAD-Net2024.03	6.777	—	—	—	—	—	—
MTL2024.03	6.797	—	—	—	—	—	—
Monodepth22021.04	6.876	0.129	1.569	0.187	0.849	0.957	0.983
Monodepth22022.03	6.876	0.129	1.569	0.187	0.849	0.957	0.983
CFCNet2019.06	6.887	—	—	—	0.889	0.961	0.981
SDC-Depth2022.06	6.92	—	—	—	—	—	—
Gordon et al.2021.02	6.96	0.127	1.33	0.195	0.83	0.947	0.981
Videos in the Wild2021.04	6.96	0.127	1.33	0.195	0.83	0.947	0.981
Videos in the Wild2022.03	6.96	0.127	1.33	0.195	0.83	0.947	0.981
Li et al.2021.02	6.98	0.119	1.29	0.19	0.846	0.952	0.982
Li et al.2021.04	6.98	0.119	1.29	0.19	0.846	0.952	0.982
Li et al.2022.03	6.98	0.119	1.29	0.19	0.846	0.952	0.982
Li et al.2024.03	6.98	0.119	1.29	0.19	0.846	0.952	0.982
Struct2Depth (M+R)2019.06	7.0237	0.1511	2.4916	0.2023	0.8255	0.9372	0.9721
Struct2Depth 22021.04	7.024	0.151	2.492	0.202	0.826	0.937	0.972
Struct2Depth 22022.03	7.024	0.151	2.492	0.202	0.826	0.937	0.972
Zhang et al.2022.06	7.1	—	—	—	—	—	—
Pad-Net2022.06	7.12	—	—	—	—	—	—
Laina et al.2022.06	7.27	—	—	—	—	—	—
Struct2Depth (M)2019.06	7.2798	0.1454	1.7368	0.2046	0.813	0.9415	0.9775
Struct2Depth2021.02	7.28	0.145	1.737	0.205	0.813	0.942	0.978
Struct2Depth 22021.04	7.28	0.145	1.737	0.205	0.813	0.942	0.976
Struct2Depth 22022.03	7.28	0.145	1.737	0.205	0.813	0.942	0.976
Struct2Depth2024.03	7.28	0.145	1.737	0.205	0.813	0.942	0.976
Pilzer et al.2021.04	8.049	0.24	4.264	0.334	0.71	0.871	0.937
Pilzer et al.2022.03	8.049	0.24	4.264	0.334	0.71	0.871	0.937
MGMNet2024.03	8.3	—	—	—	—	—	—
Struct2Depth 22021.04	8.613	0.222	5.737	0.258	0.774	0.908	0.954
Struct2Depth (R)2019.06	8.6133	0.2218	5.7374	0.2584	0.7738	0.9076	0.9542
CFCNet2019.06	9.019	—	—	—	0.828	0.941	0.972
S2R-DepthNet2021.04	11.164	0.208	2.944	0.314	0.663	0.86	0.941
Hybridnet2024.03	12.09	—	—	—	—	—	—
T2Net2021.04	13.922	0.294	4.639	0.425	0.528	0.76	0.868
Baseline2021.04	13.938	0.297	5.077	0.452	0.516	0.747	0.854
Adaptive Dual-Path Framework2026.05	—	0.0271	—	—	0.6269	—	0.899
Standard SemCom2026.05	—	0.0213	—	—	0.7071	—	0.9349