P3-SAM: Native 3D Part Segmentation

Segmenting 3D assets into their constituent parts is crucialfor enhancing 3D understanding, facilitating model reuse, and supportingvarious applications such as part generation. However, current methodsface limitations such as poor robustness when dealing with complex ob-jects and cannot fully automate the process. In this paper, we propose anative 3D point-promptable part segmentation model termed P3 -SAM,designed to fully automate the segmentation of any 3D objects into com-ponents. Inspired by SAM, P3 -SAM consists of a feature extractor, mul-tiple segmentation heads, and an IoU predictor, enabling interactive seg-mentation for users. We also propose an algorithm to automatically selectand merge masks predicted by our model for part instance segmentation.Our model is trained on a newly built dataset containing nearly 3.7 mil-lion models with reasonable segmentation labels. Comparisons show thatour method achieves precise segmentation results and strong robustnesson any complex objects, attaining state-of-the-art performance. Our codewill be released soon.