Point-SAM: Promptable 3D Segmentation Model for Point Clouds
<|reference_start|>Point-SAM: Promptable 3D Segmentation Model for Point Clouds: The development of 2D foundation models for image segmentation has been significantly advanced by the Segment Anything Model (SAM). However, achieving similar success in 3D models remains a challenge due to issues such as non-unified data formats, lightweight models, and the scarcity of labeled data with diverse masks. To this end, we propose a 3D promptable segmentation model (Point-SAM) focusing on point clouds. Our approach utilizes a transformer-based method, extending SAM to the 3D domain. We leverage part-level and object-level annotations and introduce a data engine to generate pseudo labels from SAM, thereby distilling 2D knowledge into our 3D model. Our model outperforms state-of-the-art models on several indoor and outdoor benchmarks and demonstrates a variety of applications, such as 3D annotation. Codes and demo can be found at https://github.com/zyc00/Point-SAM.<|reference_end|>
@article{zhou2024point-sam:,
    title={Point-SAM: Promptable 3D Segmentation Model for Point Clouds},
    author={Yuchen Zhou, Jiayuan Gu, Tung Yen Chiang, Fanbo Xiang, Hao Su},
    journal={arXiv preprint arXiv:2406.17741},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17741},
    primaryClass={cs.CV cs.AI}
}