Depth Estimation on KITTI 2015 benchmark leaderboard · SOTA2 Research

Depth Estimation on KITTI 2015

0.091Abs RelManydepth2

Evaluation Results

Method	Links
Manydepth22023.12		0.091	0.649	4.232	0.17	90.9	96.8	98.4
Manydepth2-NF2023.12		0.094	0.676	4.246	0.17	90.9	96.8	98.5
DCPI-Depth2023.12		0.095	0.662	4.274	0.17	90.2	96.7	98.5
Lee2023.12		0.096	0.644	4.23	0.172	90.3	96.8	98.5
DynamicDepth2023.12		0.096	0.72	4.458	0.175	89.7	96.4	98.4
ManyDepth2023.12		0.098	0.77	4.459	0.176	90	96.5	98.3
FeatDepth2023.12		0.099	0.697	4.427	0.184	88.9	96.3	98.2
Guizilini2023.12		0.102	0.698	4.381	0.178	89.6	96.4	98.4
Li2023.12		0.102	0.703	4.348	0.175	89.5	96.6	98.4
DevNet2023.12		0.103	0.713	4.459	0.177	89	96.5	98.2
FeatDepth2023.12		0.104	0.729	4.481	0.179	89.3	96.5	98.4
AQUANet2023.12		0.105	0.621	4.227	0.179	88.9	96.4	98.4
Packnet-SFM2023.12		0.107	0.802	4.538	0.186	88.9	96.2	98.1
Packnet-SFM2023.12		0.111	0.785	4.601	0.189	87.8	96	98.2
Patil2023.12		0.111	0.821	4.65	0.187	88.3	96.1	98.2
Monodepth22023.12		0.115	0.882	4.701	0.19	87.9	96.1	98.2
Monodepth22023.12		0.115	0.903	4.863	0.193	87.7	95.9	98.1
EPC++2023.12		0.128	0.935	5.011	0.209	83.1	94.5	97.9
DFR2023.12		0.135	1.132	5.585	0.229	82	93.3	97.1
EPC++2023.12		0.141	1.029	5.35	0.216	81.6	94.1	97.6
Ranjan2023.12		0.148	1.149	5.464	0.226	81.5	93.5	97.3