SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866910310287278080 |
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| author | Zhang, Dingyuan Liang, Dingkang Yang, Hongcheng Zou, Zhikang Ye, Xiaoqing Liu, Zhe Bai, Xiang |
| author_facet | Zhang, Dingyuan Liang, Dingkang Yang, Hongcheng Zou, Zhikang Ye, Xiaoqing Liu, Zhe Bai, Xiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_02245 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model Zhang, Dingyuan Liang, Dingkang Yang, Hongcheng Zou, Zhikang Ye, Xiaoqing Liu, Zhe Bai, Xiang Computer Vision and Pattern Recognition Image and Video Processing 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. |
| title | SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2306.02245 |