Monocular 3D Object Detection On Kitti Cars

AP Medium

Résultats

Résultats de performance de divers modèles sur ce benchmark

Tableau comparatif

Nom du modèle	AP Medium
is-pseudo-lidar-needed-for-monocular-3d	16.34
monodgp-monocular-3d-object-detection-with	18.72
monouni-a-unified-vehicle-and-infrastructure	16.73
consistency-of-implicit-and-explicit-features	20.95
monocular-3d-object-detection-an-extrinsic	13.87
m3d-rpn-monocular-3d-region-proposal-network	9.71
groomed-nms-grouped-mathematically	12.32
learning-geometry-guided-depth-via-projective	13.81
monopair-monocular-3d-object-detection-using	9.99
pseudo-stereo-for-monocular-3d-object	-
monorun-monocular-3d-object-detection-by-self	12.30
iafa-instance-aware-feature-aggregation-for	12.01
kinematic-3d-object-detection-in-monocular	12.72
cubifae-3d-monocular-camera-space	7.94
learning-depth-guided-convolutions-for	11.72
delving-into-localization-errors-for	12.26
gs3d-an-efficient-3d-object-detection	2.9
objects-are-different-flexible-monocular-3d	13.89
depth-conditioned-dynamic-message-propagation	12.78
probabilistic-and-geometric-depth-detecting	11.76
ground-aware-monocular-3d-object-detection	13.17
smoke-single-stage-monocular-3d-object	9.76
learning-auxiliary-monocular-contexts-helps	16.46
deviant-depth-equivariant-network-for	14.46
monogrnet-a-geometric-reasoning-network-for	5.74
cross-modality-knowledge-distillation-for	18.69
geometry-based-distance-decomposition-for	12.65
monolss-learnable-sample-selection-for	19.15
categorical-depth-distribution-network-for	13.41