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

Yuchen Zhou, Jiayuan Gu, Tung Yen Chiang, Fanbo Xiang, Hao Su

Keywords: 3D vision, promptable segmentation, point cloud segmentation

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, poor model scalability, 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. We employ an efficient transformer-based architecture tailored for point clouds, extending SAM to the 3D domain. We then distill the rich knowledge from 2D SAM for Point-SAM training by introducing a data engine to generate part-level and object-level pseudo-labels at scale from 2D SAM. Our model outperforms state-of-the-art 3D segmentation models on several indoor and outdoor benchmarks and demonstrates a variety of applications, such as interactive 3D annotation and zero-shot 3D instance proposal.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 4020