PartSAM: A Scalable Promptable Part Segmentation Model Trained on Native 3D Data

• URL: http://arxiv.org/abs/2509.21965v2
• Date: Mon, 29 Sep 2025 05:34:25 GMT
• Title: PartSAM: A Scalable Promptable Part Segmentation Model Trained on Native 3D Data
• Authors: Zhe Zhu, Le Wan, Rui Xu, Yiheng Zhang, Honghua Chen, Zhiyang Dou, Cheng Lin, Yuan Liu, Mingqiang Wei,
• Abstract summary: We present PartSAM, the first promptable part segmentation model trained on large-scale 3D data.<n>PartSAM employs an encoder-decoder architecture in which a triplane-based dual-branch encoder produces spatially structured tokens.<n>To enable large-scale supervision, we introduce a model-in-the-loop annotation pipeline that curates over five million 3D shape-part pairs.
• Score: 47.60227259482637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Segmenting 3D objects into parts is a long-standing challenge in computer vision. To overcome taxonomy constraints and generalize to unseen 3D objects, recent works turn to open-world part segmentation. These approaches typically transfer supervision from 2D foundation models, such as SAM, by lifting multi-view masks into 3D. However, this indirect paradigm fails to capture intrinsic geometry, leading to surface-only understanding, uncontrolled decomposition, and limited generalization. We present PartSAM, the first promptable part segmentation model trained natively on large-scale 3D data. Following the design philosophy of SAM, PartSAM employs an encoder-decoder architecture in which a triplane-based dual-branch encoder produces spatially structured tokens for scalable part-aware representation learning. To enable large-scale supervision, we further introduce a model-in-the-loop annotation pipeline that curates over five million 3D shape-part pairs from online assets, providing diverse and fine-grained labels. This combination of scalable architecture and diverse 3D data yields emergent open-world capabilities: with a single prompt, PartSAM achieves highly accurate part identification, and in a Segment-Every-Part mode, it automatically decomposes shapes into both surface and internal structures. Extensive experiments show that PartSAM outperforms state-of-the-art methods by large margins across multiple benchmarks, marking a decisive step toward foundation models for 3D part understanding.

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