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Depth Prediction on KITTI original ground truth (test)

0.096Abs RelDynamicDepth
0.091520.121760.1520.18224Mar 29, 2022

Evaluation results

MethodLinks
DynamicDepthTraining=Monocular, WxH=640 x 1922022.030.0960.724.4580.17589.796.498.4
ManyDepthTraining=Monocular, WxH=640 x 1922022.030.0980.774.4590.1769096.598.3
DornTraining=Supervised, WxH=513 x 3852022.030.0990.5933.7140.16189.796.698.6
FeatDepthTraining=Stereo, WxH=640 x 1922022.030.0990.6974.4270.18488.996.398.2
DepthHintTraining=Stereo, WxH=640 x 1922022.030.10.7284.4690.18588.596.298.2
Guizilini et al.Training=Monocular, WxH=640 x 1922022.030.1020.6984.3810.17889.696.498.4
FeatDepthTraining=Monocular, WxH=640 x 1922022.030.1040.7294.4810.17989.396.598.4
Wang et al.Training=Monocular, WxH=640 x 1922022.030.1060.7994.6620.18788.996.198.2
Johnston et al.Training=Monocular, WxH=640 x 1922022.030.1060.8614.6990.18588.996.298.2
MonoDepth2Training=Stereo, WxH=640 x 1922022.030.1070.8494.7640.20187.495.397.7
Gur et al.Training=Supervised, WxH=416 x 1282022.030.110.6664.1860.1688896.698.8
MonoResMatchTraining=Stereo, WxH=1280 x 3842022.030.1110.8674.7140.19986.495.497.9
Patil et al.Training=Monocular, WxH=640 x 1922022.030.1110.8214.650.18788.396.198.2
Packnet-SFMTraining=Monocular, WxH=640 x 1922022.030.1110.7854.6010.18987.89698.2
InstaDMTraining=Monocular, WxH=832 x 2562022.030.1120.7774.7720.19187.295.998.2
Kuznietsov et al.Training=Supervised, WxH=621 x 1872022.030.1130.7414.6210.18986.29698.6
Lee et al.Training=Monocular, WxH=832 x 2562022.030.1140.8764.7150.19187.295.598.1
Monodepth2Training=Monocular, WxH=640 x 1922022.030.1150.9034.8630.19387.795.998.1
SuperDepthTraining=Monocular, WxH=1024 x 3842022.030.1161.055—0.20985.394.897.7
DualNetTraining=Monocular, WxH=1248 x 3842022.030.1210.8374.9450.19785.395.598.2
MonoDispNetTraining=Stereo, WxH=512 x 2562022.030.1260.8324.1720.2178494.197.3
EPC++Training=Stereo, WxH=832 x 2562022.030.1280.9355.0110.20983.194.597.9
Videos in the wildTraining=Monocular, WxH=416 x 1282022.030.1280.9595.230.21284.594.797.6
Li et al.Training=Monocular, WxH=416 x 1282022.030.130.955.1380.20984.394.897.8
MonoDepthTraining=Stereo, WxH=512 x 2562022.030.1331.1425.5330.238393.697
SIGNetTraining=Monocular, WxH=416 x 1282022.030.1330.9055.1810.20882.594.798.1
Zhan FullNYUTraining=Supervised, WxH=608 x 1602022.030.1351.1325.5850.2298293.397.1
DFRTraining=Stereo, WxH=608 x 1602022.030.1351.1325.5850.2298293.397.1
EPC++Training=Monocular, WxH=832 x 2562022.030.1411.0295.350.21681.694.197.6
Struct2depth (M)Training=Monocular, WxH=416 x 1282022.030.1411.0265.2910.21581.694.597.9
Ranjan et al.Training=Monocular, WxH=832 x 2562022.030.1481.1495.4640.22381.593.597.3
DF-NetTraining=Monocular, WxH=576 x 1602022.030.151.1245.5070.22680.693.397.3
DDVOTraining=Monocular, WxH=416 x 1282022.030.1511.2575.5830.2288193.697.4
GeoNetTraining=Monocular, WxH=416 x 1282022.030.1551.2965.8570.23379.393.197.3
LEGOTraining=Monocular, WxH=416 x 1282022.030.1621.3526.2760.25278.392.196.9
Vid2DepthTraining=Monocular, WxH=416 x 1282022.030.1631.246.220.2576.291.696.8
UnDeepVOTraining=Stereo, WxH=512 x 1282022.030.1831.736.570.268———
SfMLearnerTraining=Monocular, WxH=416 x 1282022.030.2081.7686.9580.28367.888.595.7