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With the development of large language models, many remarkable linguisticsystems like ChatGPT have thrived and achieved astonishing success on manytasks, showing the incredible power of foundation models. In the spirit ofunleashing the capability of foundation models on vision tasks, the SegmentAnything Model (SAM), a vision foundation model for image segmentation, hasbeen proposed recently and presents strong zero-shot ability on many downstream2D tasks. However, whether SAM can be adapted to 3D vision tasks has yet to beexplored, especially 3D object detection. With this inspiration, we exploreadapting the zero-shot ability of SAM to 3D object detection in this paper. Wepropose a SAM-powered BEV processing pipeline to detect objects and getpromising results on the large-scale Waymo open dataset. As an early attempt,our method takes a step toward 3D object detection with vision foundationmodels and presents the opportunity to unleash their power on 3D vision tasks.The code is released at https://github.com/DYZhang09/SAM3D.

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