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Depth Prediction on Make3D C1 (test)

0.062Log10 Error (log10)

DORN

Updated 4d ago

Evaluation Results

Method	Links
DORN 2018.06		0.062	0.157	3.97	-	-
Resnet50 10K 2017.04		0.065	0.184	4.38	-	-
MS-CRF 2018.06		0.065	0.184	4.38	-	-
Laina et al. (Huber loss) 2017.04		0.072	0.176	4.46	-	-
Laina et al. 2018.06		0.072	0.176	4.46	-	-
Resnet50-Multi-scale 2017.04		0.076	0.206	4.51	-	-
ResNet50-Cascade 2017.04		0.082	0.213	4.67	-	-
DORN 2018.06		0.082	0.236	7.02	-	-
Laina et al. 2016.09		0.082	-	5.461	1.665	0.198
Laina et al. (l2 loss) 2017.04		0.089	0.223	4.89	-	-
Li et al. 2017.04		0.092	0.278	7.19	-	-
Li et al. 2018.06		0.092	0.278	7.12	-	-
Liu et al. 2018.06		0.109	0.287	7.36	-	-
Liu et al. [20] 2017.04		0.119	0.314	8.6	-	-
Karsch et al. 2017.04		0.127	0.355	9.2	-	-
DepthTransfer 2018.06		0.127	0.355	9.2	-	-
Liu et al. [21] 2017.04		0.137	0.335	9.49	-	-
Liu et al. 2018.06		0.137	0.335	9.49	-	-
monodepth 2016.09		0.142	-	8.86	7.112	0.443
Karsch et al. 2016.09		0.144	-	8.172	4.894	0.417
LRC 2018.06		0.156	0.443	11.513	-	-
monodepth 2016.09		0.156	-	11.513	11.99	0.535
Liu et al. 2016.09		0.161	-	9.972	6.625	0.462
Kuznietsov et al. 2018.06		0.19	0.421	8.24	-	-
Train set mean 2016.09		0.223	-	11.542	15.517	0.893
LRC-Deep3D 2018.06		2.527	1	19.11	-	-
monodepth 2016.09		2.527	-	19.11	17.18	1