Monocular Depth Estimation on KITTI 2015 (Eigen split)

0.072Abs RelDORN

Evaluation Results

Method
DORN2019.09	0.072	0.307	2.727	0.12	93.2	98.4	99.4
SVSM finetuned2019.09	0.094	0.626	4.252	0.177	89.1	96.5	98.4
Ours HR Resnet502019.09	0.096	0.71	4.393	0.185	89	96.2	98.1
CADepth-Net2021.12	0.096	0.694	4.264	0.173	90.8	96.8	98.4
DVSO2019.09	0.097	0.734	4.442	0.187	88.8	95.8	98
Guo StereoSupFTAll -> Mono pt2019.09	0.097	0.653	4.17	0.17	88.9	96.7	98.6
Ours HR2019.09	0.098	0.702	4.398	0.183	88.7	96.3	98.3
DepthHints2021.12	0.098	0.702	4.398	0.183	88.7	96.3	98.3
Guo StereoUnsupFT -> Mono pt2019.09	0.099	0.745	4.424	0.182	88.4	96.3	98.3
Ours HR2019.09	0.099	0.723	4.445	0.187	88.6	96.2	98.1
FeatDepth2021.12	0.099	0.697	4.427	0.184	88.9	96.3	98.2
HR-Depth2021.12	0.101	0.716	4.395	0.179	89.9	96.6	98.3
SVSM w/o finetuning2019.09	0.102	0.7	4.681	0.2	87.2	95.4	97.8
Ours Resnet502019.09	0.102	0.762	4.602	0.189	88	96	98.1
CADepth-Net2021.12	0.102	0.734	4.407	0.178	89.8	96.6	98.4
CADepth-Net2021.12	0.102	0.715	4.312	0.176	90	96.8	98.4
CADepth-Net2021.12	0.102	0.752	4.504	0.181	89.4	96.4	98.3
Monodepth22019.09	0.104	0.786	4.687	0.194	87.6	95.8	98
Monodepth22019.09	0.104	0.775	4.562	0.191	87.8	95.9	98.1
FeatDepth2021.12	0.104	0.729	4.481	0.179	89.3	96.5	98.4
HR-Depth2021.12	0.104	0.727	4.41	0.179	89.4	96.6	98.4
Monodepth22019.09	0.105	0.822	4.692	0.199	87.6	95.4	97.7
Ours2019.09	0.105	0.769	4.627	0.189	87.5	95.9	98.2
CADepth-Net2021.12	0.105	0.769	4.535	0.181	89.2	96.4	98.3
DepthHints2021.12	0.105	0.769	4.627	0.189	87.5	95.9	98.2
Ours2019.09	0.106	0.78	4.695	0.193	87.5	95.8	98
Johnston et al.2021.12	0.106	0.861	4.699	0.185	88.9	96.2	98.2
HR-Depth2021.12	0.106	0.755	4.472	0.181	89.2	96.6	98.4
Monodepth22021.12	0.106	0.818	4.75	0.196	87.4	95.7	97.9
Monodepth22021.12	0.106	0.806	4.63	0.193	87.6	95.8	98
DVSO SimpleNet2019.09	0.107	0.852	4.785	0.199	86.6	95	97.8
PackNet-SfM2021.12	0.107	0.802	4.538	0.186	88.9	96.2	98.1
HR-Depth2021.12	0.107	0.785	4.612	0.185	88.7	96.2	98.2
Kuznietsov et al. [20]2018.08	0.108	0.595	3.518	0.179	87.5	96.4	98.8
Monodepth22019.09	0.108	0.842	4.891	0.207	86.6	94.9	97.6
HR-Depth2021.12	0.109	0.792	4.632	0.185	88.4	96.2	98.3
monoResMatch2019.09	0.111	0.867	4.714	0.199	86.4	95.4	97.9
PackNet-SfM2021.12	0.111	0.785	4.601	0.189	87.8	96	98.2
SuperDepth2019.09	0.112	0.875	4.958	0.207	85.2	94.7	97.7
Ours HR Resnet50 w/o pretraining2019.09	0.112	0.857	4.807	0.203	86.1	95.2	97.8
Kuznietsov2019.09	0.113	0.741	4.621	0.189	86.2	96	98.6
TrianFlow2021.12	0.113	0.704	4.581	0.184	87.1	96.1	98.4
SGDepth2021.12	0.113	0.88	4.695	0.192	88.4	96.1	98.1
Our fr, all-real2018.08	0.114	0.627	3.549	0.178	86.7	96	98.6
Monodepth22021.12	0.115	0.903	4.863	0.193	87.7	95.9	98.1
Monodepth22021.12	0.115	0.882	4.701	0.19	87.9	96.1	98.2
CADepth-Net2021.12	0.116	0.893	4.906	0.192	87.4	95.7	98.1
Godard et al. [10]2018.08	0.117	0.762	3.972	0.206	86	94.8	97.6
SGDepth2021.12	0.117	0.907	4.844	0.196	87.5	95.8	98
Ours Resnet50 w/o pretraining2019.09	0.118	0.941	5.055	0.21	85	94.8	97.6
DualNet2021.12	0.121	0.837	4.945	0.197	85.3	95.5	98.2
Godard et al.2018.03	0.124	1.076	5.311	0.219	84.7	94.2	97.3
3Net (Resnet50)2019.09	0.126	0.961	5.205	0.22	83.5	94.1	97.4
StrAT2019.09	0.128	1.019	5.403	0.227	82.7	93.5	97.1
Monodepth2 (w/o pretraining)2019.09	0.128	1.089	5.385	0.229	83.2	93.4	96.9
EPC++2019.09	0.128	0.935	5.011	0.209	83.1	94.5	97.9
SGDepth2021.12	0.128	1.003	5.085	0.206	85.3	95.1	97.8
Monodepth22021.12	0.128	1.087	5.171	0.204	85.5	95.3	97.8
EPC++2021.12	0.128	0.935	5.011	0.209	83.1	94.5	97.9
SIGNet2018.12	0.133	0.905	5.181	0.208	82.5	94.7	98.1
SIGNet2021.12	0.133	0.905	5.181	0.208	82.5	94.7	98.1
Zhan2019.09	0.135	1.132	5.585	0.229	82	93.3	97.1
Monodepth2019.09	0.138	1.186	5.65	0.234	81.3	93	96.9
CC2021.12	0.14	1.07	5.326	0.217	82.6	94.1	97.5
Struct2depth2021.12	0.141	1.026	5.291	0.215	81.6	94.5	97.9
GeoNet (ResNet)2018.03	0.147	0.936	4.348	0.218	81	94.1	97.7
Godard et al.2018.03	0.148	1.344	5.927	0.247	80.3	92.2	96.4
Godard et al.2018.12	0.148	1.344	5.927	0.247	80.3	92.2	96.4
GeoNet2021.12	0.149	1.06	5.567	0.226	79.6	93.5	97.5
DF-Net2021.12	0.15	1.124	5.507	0.223	80.6	93.3	97.3
DDVO2021.12	0.151	1.257	5.583	0.228	81	93.6	97.4
GeoNet (ResNet)2018.03	0.153	1.328	5.737	0.232	80.2	93.4	97.2
GeoNet (ResNet)2018.03	0.155	1.296	5.857	0.233	79.3	93.1	97.3
Yin et al.2018.12	0.155	1.296	5.857	0.233	79.3	93.1	97.3
GeoNet (VGG)2018.03	0.157	0.99	4.6	0.231	78.1	93.1	97.4
GeoNet (VGG)2018.03	0.164	1.303	6.09	0.247	76.5	91.9	96.8
Our T2Net, Dimage only2018.08	0.168	1.199	4.674	0.243	77.2	91.2	96.6
Garg et al.2018.03	0.169	1.08	5.104	0.273	74	90.4	96.2
Garg et al. [7] L12 Aug.8x2018.08	0.169	1.08	5.104	0.273	74	90.4	96.2
Our full T2Net2018.08	0.169	1.23	4.717	0.245	76.9	91.2	96.5
Zhou et al. updated2018.03	0.183	1.595	6.709	0.27	73.4	90.2	95.9
Zhou et al. updated2018.12	0.183	1.595	6.709	0.27	73.4	90.2	95.9
SfMLeaner2021.12	0.183	1.595	6.709	0.27	73.4	90.2	95.9
Eigen et al. [4] Fine2018.08	0.19	1.515	7.156	0.27	69.2	89.9	96.7
Zhou et al.2018.03	0.198	1.836	6.565	0.275	71.8	90.1	96
Liu et al.2018.03	0.202	1.614	6.523	0.275	67.8	89.5	96.5
Liu et al.2018.12	0.202	1.614	6.523	0.275	67.8	89.5	96.5
Eigen et al. Fine2018.03	0.203	1.548	6.307	0.282	70.2	89	95.8
Eigen et al. Fine2018.12	0.203	1.548	6.307	0.282	70.2	89	95.7
Zhou et al.2018.03	0.208	1.768	6.856	0.283	67.8	88.5	95.7
Eigen et al. Coarse2018.03	0.214	1.605	6.563	0.292	67.3	88.4	95.7
Eigen et al. Coarse2018.12	0.214	1.605	6.653	0.292	67.3	88.4	95.7
Our T2Net, Dfeat only2018.08	0.233	2.902	6.285	0.3	74.3	88	93.8
Our fr, all-synthetic2018.08	0.278	3.216	6.268	0.322	68.1	85.4	92.9
Baseline, train set mean2018.08	0.521	11.024	10.598	0.473	63.8	75.5	83.5