3D Object Detection on KITTI car category (val) benchmark leaderboard · SOTA2 Research

3D Object Detection on KITTI car category (val)

89.4AP BEV EasyAVOD-FPN (Baseline)

Evaluation Results

Method	Links
AVOD-FPN (Baseline)2024.09		89.4	82.4	83.9	72.2	78.7	66.5
AlterMOMA2024.09		87.2	80.5	81.5	70.2	77.9	63.2
AlterMOMA2024.09		85.3	77.4	79.9	68.2	75.8	62.3
ProsPr2024.09		85.2	78.9	79.1	69.6	75.7	62.1
ProsPr2024.09		81.2	74.2	75.1	63.4	71.9	59.1
SynFlow2024.09		79.5	74.2	75.3	65.7	70.3	60.2
SNIP2024.09		79.1	73.5	75.8	64.9	69.6	59.8
SynFlow2024.09		73.5	64.5	67.6	54.7	64.4	48.1
SNIP2024.09		72.4	62.7	66.9	52.3	63.7	45.8
IMP2024.09		69.2	65.8	64.6	57.7	59.7	51.3
GUPNet2023.03		61.78	57.62	47.06	42.33	40.88	37.59
MonoPair2023.03		61.06	55.38	47.63	42.39	41.92	37.99
MonoDLE2023.03		60.73	55.41	46.87	43.42	41.89	37.81
IMP2024.09		59.6	52.1	54.2	45.7	51.7	43.2
WeakM3D2023.03		58.2	50.16	38.02	29.94	30.17	23.11
WeakMono3D2023.03		54.32	49.37	42.83	39.01	40.07	36.34
M3D-RPN2023.03		53.35	48.53	39.6	35.94	31.76	28.59
MonoGRNet2023.03		52.13	47.59	35.99	32.28	28.72	25.5
Autolabels2023.03		50.51	38.31	30.97	19.9	23.72	14.83
CenterNet2023.03		34.36	20	27.91	17.5	24.65	15.57
VS3D2023.03		31.59	22.62	20.59	14.43	16.28	10.91