Auto-Vocabulary 3D Object Detection on ScanNet V2 (val) benchmark leaderboard · SOTA2 Research

Auto-Vocabulary 3D Object Detection on ScanNet V2 (val)

9.23mAPAV3DOD

Evaluation Results

Method	Links
AV3DOD2025.12		9.23	20.04	7.07	27.2	47.98	23.04	0.89	0.797	0.812	0.585	0.709	0.475	0.57
CoDA2025.12		5.75	16.87	3.52	18.82	40.21	14.55	0.885	0.68	0.804	0.448	0.602	0.36	0.458
3DETR+Florence22025.12		5.16	18.05	2.58	15.87	40.53	10.94	0.876	0.651	0.806	0.421	0.57	0.339	0.433
3DETR+Qwen32025.12		4.95	16.31	2.67	15.49	36.57	11.28	0.911	0.622	0.801	0.395	0.567	0.316	0.418
3DETR+PointLLM2025.12		3.8	13.84	1.79	12.75	25.89	10.12	0.875	0.612	0.775	0.384	0.536	0.298	0.394
3DETR+Qwen32025.12		3.18	11.85	3.18	9.93	22.59	7.39	0.887	0.618	0.749	0.369	0.548	0.276	0.387
3DETR+PointLLM2025.12		2.84	14.89	0.43	6.88	32.77	1.71	0.896	0.616	0.754	0.369	0.552	0.278	0.389
3DETR+Florence22025.12		2.83	13.82	0.63	8.06	28	4.07	0.867	0.616	0.749	0.371	0.534	0.278	0.382