Monocular Depth Estimation on KITTI 2015 (test) benchmark leaderboard · SOTA2 Research

Monocular Depth Estimation on KITTI 2015 (test)

0.105Abs RelOurs without uncert. (V1)

Evaluation Results

Method
Ours without uncert. (V1)2021.10	0.105	0.768	4.539	0.182	0.89	0.964	0.983
Johnston et al.2021.10	0.106	0.861	4.699	0.185	0.889	0.962	0.982
Ours with uncert. (V2)2021.10	0.106	0.75	4.482	0.182	0.891	0.964	0.983
HR-Depth2021.10	0.109	0.792	4.632	0.185	0.884	0.962	0.983
PackNet-SFM2021.10	0.111	0.785	4.601	0.189	0.878	0.96	0.982
Learn Stereo, Infer Mono (ResNet)2019.05	0.113	0.898	5.048	0.208	0.853	0.948	0.976
Godard et al.2019.05	0.114	0.898	4.935	0.206	0.861	0.949	0.976
MonoDepth22021.10	0.115	0.903	4.863	0.193	0.877	0.959	0.981
Ours2026.05	0.116	0.892	4.892	0.195	—	—	—
OmegaNet2021.10	0.12	0.792	4.75	0.191	0.856	0.958	0.984
Learn Stereo, Infer Mono (VGG)2019.05	0.121	0.9643	5.137	0.213	0.846	0.944	0.976
CoopNet2026.05	0.125	1.157	5.251	0.209	—	—	—
Gordon et al.2021.10	0.128	0.959	5.23	0.212	0.845	0.947	0.976
GLNet2026.05	0.129	1.044	5.361	0.212	—	—	—
GLNet2021.10	0.135	1.07	5.23	0.21	0.841	0.948	0.98
Yin et al.2019.05	0.153	1.328	5.737	0.232	0.802	0.934	0.972
Zhou et al.2019.05	0.198	1.836	6.565	0.275	0.718	0.901	0.96
Liu et al.2019.05	0.202	1.614	6.523	0.275	0.678	0.895	0.965
Eigen et al. - Fine2019.05	0.203	1.548	6.307	0.282	0.702	0.89	0.958
SFMlearner2021.10	0.208	1.768	6.958	0.283	0.678	0.885	0.957
Eigen et al. - Coarse2019.05	0.214	1.605	6.563	0.292	0.673	0.884	0.957
Train set mean2019.05	0.361	4.826	8.102	0.377	0.638	0.804	0.894