用超点提升机器人对可动物体的部件分割精度，尤其在未见类别上表现优异。
Generalizable Articulated Object Perception with Superpoints
- 基于几何与语义相似性生成可学习的超点，更好捕捉部件边界。
- 在已见类别上达77.9% AP50（提升4.4%），未见类别39.3%（提升11.6%）。
- 适合需要跨类别泛化的机器人操作感知任务，如机械臂抓取。
机器人操控可动物体因复杂运动结构而困难，需精准部件分割以实现高效操作。本文提出一种基于超点的新型感知方法，通过可学习、部件感知的超点生成技术，根据几何与语义相似性高效聚类点云，获得更清晰的部件边界。结合2D基础模型SAM识别像素区域中心，并选取对应超点作为候选查询点，再利用基于查询的Transformer解码器进一步提升分割精度。在GAPartNet数据集上的实验表明，该方法在跨类别部件分割上优于现有最先进方法：已见类别AP50达77.9%（提升4.4%），未见类别达39.3%（提升11.6%），在9个部件类别中，已见类有5个表现最优，未见类所有类别均超越此前方法。
原文摘要 · Abstract (English)
Manipulating articulated objects with robotic arms is challenging due to the complex kinematic structure, which requires precise part segmentation for efficient manipulation. In this work, we introduce a novel superpoint-based perception method designed to improve part segmentation in 3D point clouds of articulated objects. We propose a learnable, part-aware superpoint generation technique that efficiently groups points based on their geometric and semantic similarities, resulting in clearer part boundaries. Furthermore, by leveraging the segmentation capabilities of the 2D foundation model SAM, we identify the centers of pixel regions and select corresponding superpoints as candidate query points. Integrating a query-based transformer decoder further enhances our method's ability to achieve precise part segmentation. Experimental results on the GAPartNet dataset show that our method outperforms existing state-of-the-art approaches in cross-category part segmentation, achieving AP50 scores of 77.9% for seen categories (4.4% improvement) and $39.3\%$ for unseen categories (11.6% improvement), with superior results in 5 out of 9 part categories for seen objects and outperforming all previous methods across all part categories for unseen objects.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。