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Single-view Depth Estimation on NYU official 654 images v2 (test)

0.108AbsRel

VNL

Updated 4d ago

Evaluation Results

Method	Links
VNL 2020.06		0.108	0.048	0.416	87.5	97.6	99.4
DORN 2020.06		0.115	0.051	0.509	82.8	96.5	99.2
Laina et al. 2020.06		0.127	0.055	0.573	81.1	95.3	98.8
Ours-ARN 2020.06		0.138	0.059	0.532	82	95.6	98.9
Li et al. 2020.06		0.143	0.063	0.635	78.8	95.8	99.1
Ours-DP 2020.06		0.143	0.06	0.538	81.2	95.1	98.6
P2Net 2020.06		0.147	0.062	0.553	80.1	95.1	98.7
Chakrabarti et al. 2020.06		0.149	-	0.62	80.6	95.8	98.7
Eigen et al. 2020.06		0.158	-	0.641	76.9	95	98.8
P2Net 2020.06		0.159	0.068	0.599	77.2	94.2	98.4
SC-Depth 2020.06		0.159	0.068	0.608	77.2	93.9	98.2
Monodepth2 2020.06		0.176	0.074	0.639	73.4	93.7	98.3
Roy et al. 2020.06		0.187	0.078	0.744	-	-	-
TrainFlow 2020.06		0.189	0.079	0.686	70.1	91.2	97.8
MovingIndoor 2020.06		0.208	0.086	0.712	67.4	90	96.8
Liu et al. 2020.06		0.213	0.087	0.759	65	90.6	97.6
Wang et al. 2020.06		0.22	0.094	0.745	60.5	89	97
Li et al. 2020.06		0.232	0.094	0.821	62.1	88.6	96.8
Liu et al. 2020.06		0.335	0.127	1.06	-	-	-
Make3D 2020.06		0.349	0.1214	1.214	44.7	74.5	89.7
Depth Transfer 2020.06		0.349	0.131	1.21	-	-	-
Ladicky et al. 2020.06		-	-	-	54.2	82.9	94.1