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We introduce SAMPro3D for zero-shot 3D indoor scene segmentation. Given the3D point cloud and multiple posed 2D frames of 3D scenes, our approach segments3D scenes by applying the pretrained Segment Anything Model (SAM) to 2D frames.Our key idea involves locating 3D points in scenes as natural 3D prompts toalign their projected pixel prompts across frames, ensuring frame-consistencyin both pixel prompts and their SAM-predicted masks. Moreover, we suggestfiltering out low-quality 3D prompts based on feedback from all 2D frames, forenhancing segmentation quality. We also propose to consolidate different 3Dprompts if they are segmenting the same object, bringing a more comprehensivesegmentation. Notably, our method does not require any additional training ondomain-specific data, enabling us to preserve the zero-shot power of SAM.Extensive qualitative and quantitative results show that our methodconsistently achieves higher quality and more diverse segmentation thanprevious zero-shot or fully supervised approaches, and in many cases evensurpasses human-level annotations. The project page can be accessed athttps://mutianxu.github.io/sampro3d/.

SAMPro3D: Locating SAM Prompts in 3D for Zero-Shot Scene Segmentation