Depth Prediction on KITTI original (Eigen split) benchmark leaderboard · SOTA2 Research

Depth Prediction on KITTI original (Eigen split)

0.063Abs RelFusionDepth (Refined Depth + GDC)

Evaluation Results

Method	Links
FusionDepth (Refined Depth + GDC)2021.09		0.063	0.364	3.291	0.139	94.5	97.8	98.8
FusionDepth (Initial Depth + GDC)2021.09		0.067	0.423	3.42	0.144	94.1	97.7	98.8
FusionDepth (Refined Depth)2021.09		0.074	0.423	3.61	0.15	93.6	97.3	98.6
FusionDepth (Initial Depth)2021.09		0.078	0.515	3.67	0.154	93.5	97.3	98.6
Guizilini et al.2021.09		0.082	0.424	3.73	0.131	91.7	—	—
BTS2021.09		0.091	0.555	4.033	0.174	90.4	96.7	98.4
DynamicDepth2022.03		0.096	0.72	4.458	0.175	89.7	96.4	98.4
MonoPL++2021.09		0.098	0.714	4.3	0.176	89.9	96.7	98.4
ManyDepth2022.03		0.098	0.77	4.459	0.176	90	96.5	98.3
Dorn2021.09		0.099	0.593	3.714	0.161	89.7	96.6	98.6
GLNet2021.09		0.099	0.796	4.743	0.186	88.4	95.5	97.9
Guizilini et al.2022.03		0.102	0.698	4.381	0.178	89.6	96.4	98.4
MonoDepth22021.09		0.106	0.818	4.75	0.196	87.4	95.7	97.9
Johnston et al.2022.03		0.106	0.861	4.699	0.185	88.9	96.2	98.2
Wang et al.2022.03		0.106	0.799	4.662	0.187	88.9	96.1	98.2
Struct2Depth2021.09		0.109	0.825	4.75	0.187	87.4	95.8	98.3
PackNet-SfM2021.09		0.111	0.785	4.601	0.189	87.8	96	98.2
Packnet-SFM2022.03		0.111	0.785	4.601	0.189	87.8	96	98.2
Patil et al.2022.03		0.111	0.821	4.65	0.187	88.3	96.1	98.2
InstaDM2022.03		0.112	0.777	4.772	0.191	87.2	95.9	98.2
Lee et al.2022.03		0.114	0.876	4.715	0.191	87.2	95.5	98.1
Monodepth22022.03		0.115	0.903	4.863	0.193	87.7	95.9	98.1
Videos in the wild2022.03		0.128	0.959	5.23	0.212	84.5	94.7	97.6
Li et al.2022.03		0.13	0.95	5.138	0.209	84.3	94.8	97.8
MonoDepth2021.09		0.133	1.142	5.533	0.23	83	93.6	97
EPC++2022.03		0.141	1.029	5.35	0.216	81.6	94.1	97.6
Struct2depth (M)2022.03		0.141	1.026	5.291	0.215	81.6	94.5	97.9
Ranjan et al.2022.03		0.148	1.149	5.464	0.226	81.5	93.5	97.3
LEGO2021.09		0.162	1.352	6.276	0.252	78.3	92.1	96.9