P3-SAM实现3D物体全自动部件分割，精度高且对复杂对象鲁棒。
P3-SAM: Native 3D Part Segmentation
- 基于点提示的3D分割模型，支持交互式与自动化分割。
- 在近370万模型上训练，对复杂物体分割准确率领先。
- 适合需要高效3D部件拆分的研究与工业应用。
将3D资产分割为组成部分对于提升3D理解、促进模型复用及支持各类应用（如部件生成）至关重要。然而，现有方法在处理复杂物体时鲁棒性差，且无法完全自动化。本文提出一种原生3D点提示分割模型P³-SAM，旨在全自动分割任意3D物体为组件。受SAM启发，P³-SAM包含特征提取器、多个分割头和IoU预测器，支持用户交互式分割。我们还提出算法，自动选择并合并模型预测的掩码以实现部件实例分割。模型在新构建的数据集上训练，该数据集包含近370万带有合理分割标签的模型。实验表明，本方法在复杂物体上实现了高精度分割与强鲁棒性，达到当前最优性能。
原文摘要 · Abstract (English)
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 P$^3$-SAM, designed to fully automate the segmentation of any 3D objects into components. Inspired by SAM, P$^3$-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 project page is available at https://murcherful.github.io/P3-SAM/.
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