P3-SAM：原生三维部件分割

摘要

将三维资产分割成其组成部分对于提升三维理解、促进模型复用以及支持诸如部件生成等多种应用至关重要。然而，现有方法在处理复杂物体时存在鲁棒性不足的问题，且无法实现全自动化流程。本文提出了一种原生三维点提示部件分割模型，命名为P3-SAM，旨在实现对任意三维物体组件的全自动分割。受SAM启发，P3-SAM由特征提取器、多个分割头及IoU预测器组成，支持用户进行交互式分割。我们还提出了一种算法，用于自动选择并合并模型预测的掩码，以实现部件实例分割。我们的模型在一个新构建的数据集上训练，该数据集包含近370万个带有合理分割标签的模型。对比实验表明，我们的方法在任意复杂物体上均能实现精确分割结果，展现出极强的鲁棒性，达到了业界领先水平。代码即将发布。

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 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：原生三维部件分割

P3-SAM: Native 3D Part Segmentation

摘要

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