Segmenting 3D assets into their constituent parts is crucial for enhancing 3D
understanding, facilitating model reuse, and supporting various applications
such as part generation. However, current methods face limitations such as poor
robustness when dealing with complex objects and cannot fully automate the
process. In this paper, we propose a native 3D point-promptable part
segmentation model termed P3-SAM, designed to fully automate the segmentation
of any 3D objects into components. Inspired by SAM, P3-SAM consists of a
feature extractor, multiple segmentation heads, and an IoU predictor, enabling
interactive segmentation for users. We also propose an algorithm to
automatically select and merge masks predicted by our model for part instance
segmentation. Our model is trained on a newly built dataset containing nearly
3.7 million models with reasonable segmentation labels. Comparisons show that
our method achieves precise segmentation results and strong robustness on any
complex objects, attaining state-of-the-art performance. Our code will be
released soon.