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SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model

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arxiv 2306.02245 v2 pith:WBCJPRI2 submitted 2023-06-04 cs.CV eess.IV

Dingyuan Zhang , Dingkang Liang , Hongcheng Yang , Zhikang Zou , Xiaoqing Ye , Zhe Liu , Xiang Bai This is my paper · ORCID
classification cs.CVeess.IV
keywords tasksvisiondetectionfoundationmodelsobjectmanymodel
verification ladder T0 review T1 audit T2 compute T3 formal
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With the development of large language models, many remarkable linguistic systems like ChatGPT have thrived and achieved astonishing success on many tasks, showing the incredible power of foundation models. In the spirit of unleashing the capability of foundation models on vision tasks, the Segment Anything Model (SAM), a vision foundation model for image segmentation, has been proposed recently and presents strong zero-shot ability on many downstream 2D tasks. However, whether SAM can be adapted to 3D vision tasks has yet to be explored, especially 3D object detection. With this inspiration, we explore adapting the zero-shot ability of SAM to 3D object detection in this paper. We propose a SAM-powered BEV processing pipeline to detect objects and get promising results on the large-scale Waymo open dataset. As an early attempt, our method takes a step toward 3D object detection with vision foundation models and presents the opportunity to unleash their power on 3D vision tasks. The code is released at https://github.com/DYZhang09/SAM3D.

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Cited by 6 Pith papers

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