P3-SAM：原生3D部件分割

摘要

將3D資產分割成其組成部分對於增強3D理解、促進模型重用以及支持部件生成等各種應用至關重要。然而，現有方法在處理複雜物體時面臨魯棒性差等限制，且無法完全自動化這一過程。本文提出了一種原生3D點提示部件分割模型，稱為P3-SAM，旨在實現對任何3D物體進行組件分割的全自動化。受SAM啟發，P3-SAM由特徵提取器、多個分割頭和一個IoU預測器組成，支持用戶進行交互式分割。我們還提出了一種算法，用於自動選擇和合併模型預測的掩碼，以實現部件實例分割。我們的模型在一個新構建的數據集上進行訓練，該數據集包含近370萬個具有合理分割標籤的模型。對比結果表明，我們的方法在任何複雜物體上都能實現精確的分割結果和強大的魯棒性，達到了最先進的性能。我們的代碼即將發布。

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3D部件分割

P3-SAM: Native 3D Part Segmentation

摘要

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