Point-SAM: Promptable 3D Segmentation Model for Point Clouds

• URL: http://arxiv.org/abs/2406.17741v1
• Date: Tue, 25 Jun 2024 17:28:03 GMT
• Title: Point-SAM: Promptable 3D Segmentation Model for Point Clouds
• Authors: Yuchen Zhou, Jiayuan Gu, Tung Yen Chiang, Fanbo Xiang, Hao Su,
• Abstract summary: 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. Our model outperforms state-of-the-art models on several indoor and outdoor benchmarks.
• Score: 25.98791840584803
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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