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3D Object Detection On Nuscenes

المقاييس

NDS
mAAE
mAOE
mAP
mASE
mATE
mAVE

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج
NDS
mAAE
mAOE
mAP
mASE
mATE
mAVE
Paper TitleRepository
xin.lu.20.520.130.420.430.260.60.5--
LargeKernel-F0.740.130.30.710.230.240.24--
pcd_lidar_990.70.130.340.640.240.260.22--
MVP0.710.130.320.660.240.260.31Multimodal Virtual Point 3D Detection-
3D Dual-Fusion0.710.120.360.680.240.270.26--
SECOND + PointPillars0.180.590.960.090.420.71.0--
pointpainting0.610.130.540.540.260.380.29--
TiG-BEV0.620.130.340.530.240.450.31--
weareateam0.380.180.540.30.270.721.17--
Deeplearner0.730.130.340.710.240.250.26--
BEVFusion0.720.130.370.690.250.260.27--
Vidar0.450.130.440.380.250.631.48--
CenterFusion0.450.110.520.330.260.630.61--
PGD0.45--0.39---Probabilistic and Geometric Depth: Detecting Objects in Perspective-
DAMEN0.580.120.390.460.250.460.33--
picolo0.710.120.370.670.240.270.27--
ASCNet-1-5s0.570.140.420.450.250.320.42--
obj_40.590.140.380.510.260.450.39--
yangfan2930.610.150.430.540.260.40.36--
Radiant0.580.420.330.620.240.281.35--
0 of 372 row(s) selected.
3D Object Detection On Nuscenes | SOTA | HyperAI