P3-SAM: Native 3D Part Segmentation
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.
68bfa444207285de11b07c13
Changfeng Ma
claimed_verified
2025-09-09T13:47:28.483Z
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/avatars/46f2ae3717d1dfa5773d419e4dc9a891.svg
Changhangfeng MA
user
murcherful
68bfa444207285de11b07c14
Yang Li
68bfa444207285de11b07c15
Xinhao Yan
claimed_verified
2025-09-12T16:20:00.639Z
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/avatars/dc19ba1080b0b17a220d7e52bd514f13.svg
Xinhao Yan
user
HowieYan
68bfa444207285de11b07c16
Jiachen Xu
68bfa444207285de11b07c17
Yunhan Yang
68bfa444207285de11b07c18
Chunshi Wang
68bfa444207285de11b07c19
Zibo Zhao
68bfa444207285de11b07c1a
Yanwen Guo
68bfa444207285de11b07c1b
Zhuo Chen
68bfa444207285de11b07c1c
Chunchao Guo
P3-SAM: Native 3D Part Segmentation
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.
P3-SAM, a native 3D point-promptable part segmentation model, achieves precise and robust segmentation of complex 3D objects using a feature extractor, multiple segmentation heads, and an IoU predictor.
3D point-promptable part segmentation P3-SAM feature extractor segmentation heads IoU predictor interactive segmentation part instance segmentation dataset