Part2Object：分层无监督3D实例分割
Part2Object: Hierarchical Unsupervised 3D Instance Segmentation
AI总结：
针对无监督3D实例分割中聚类松紧不当导致的分割误差问题，提出带物体引导的分层聚类方法Part2Object及配套的Hi-Mask3D模型，利用2D帧的3D物体性先验引导多层聚类，在多场景下取得优于现有最优模型的性能。
摘要：
无监督3D实例分割旨在无需任何标注的情况下从3D点云中分割出物体。现有方法面临聚类过松或过紧的挑战，会导致欠分割或过分割问题。为解决该问题，我们提出Part2Object，一种带物体引导的分层聚类方法。Part2Object采用从点到物体部件再到物体的多层聚类机制，允许物体在任意层级显现。此外，它从时间连续的2D RGB帧中提取并利用3D物体性先验来引导聚类过程。我们还提出Hi-Mask3D以支持分层3D物体部件与实例分割。通过在Part2Object提取的物体和物体部件上训练Hi-Mask3D，我们在无监督实例分割、数据高效微调以及跨数据集泛化等多种场景下，均取得了优于现有最优模型的稳定性能。代码已发布于https://github.com/ChengShiest/Part2Object
英文摘要：
Unsupervised 3D instance segmentation aims to segment objects from a 3D point cloud without any annotations. Existing methods face the challenge of either too loose or too tight clustering, leading to under-segmentation or over-segmentation. To address this issue, we propose Part2Object, hierarchical clustering with object guidance. Part2Object employs multi-layer clustering from points to object parts and objects, allowing objects to manifest at any layer. Additionally, it extracts and utilizes 3D objectness priors from temporally consecutive 2D RGB frames to guide the clustering process. Moreover, we propose Hi-Mask3D to support hierarchical 3D object part and instance segmentation. By training Hi-Mask3D on the objects and object parts extracted from Part2Object, we achieve consistent and superior performance compared to state-of-the-art models in various settings, including unsupervised instance segmentation, data-efficient fine-tuning, and cross-dataset generalization. Code is release at https://github.com/ChengShiest/Part2Object
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
