GAPartManip：面向材料无关铰接物体操作的大规模部件中心数据集
GAPartManip: A Large-scale Part-centric Dataset for Material-Agnostic Articulated Object Manipulation
AI总结：
提出大规模部件中心数据集GAPartManip，结合照片级材质随机化和部件级交互姿态标注，通过模块化框架提升深度估计与交互姿态预测，在仿真和真实场景中实现鲁棒的铰接物体操作。
摘要：
有效操作家庭场景中的铰接物体是实现通用具身人工智能的关键一步。3D视觉的主流研究主要关注通过深度感知和姿态检测进行操作。然而，在真实环境中，由于深度感知不完美（如透明盖子和反光把手），这些方法常面临挑战。此外，它们普遍缺乏灵活适应操作所需的部件级交互多样性。为解决这些问题，我们引入了一个大规模部件中心的铰接物体操作数据集，该数据集具有照片级材质随机化和详细的部件导向、场景级可操作交互姿态标注。我们通过将数据集与多种最先进的深度估计和交互姿态预测方法集成，评估了其有效性。此外，我们提出了一种新颖的模块化框架，为可泛化的铰接物体操作提供了优越且鲁棒的性能。大量实验表明，我们的数据集在仿真和真实场景中显著提升了深度感知和可操作交互姿态预测的性能。更多信息和演示请访问：此 https URL。
英文摘要：
Effectively manipulating articulated objects in household scenarios is a crucial step toward achieving general embodied artificial intelligence. Mainstream research in 3D vision has primarily focused on manipulation through depth perception and pose detection. However, in real-world environments, these methods often face challenges due to imperfect depth perception, such as with transparent lids and reflective handles. Moreover, they generally lack the diversity in part-based interactions required for flexible and adaptable manipulation. To address these challenges, we introduced a large-scale part-centric dataset for articulated object manipulation that features both photo-realistic material randomization and detailed annotations of part-oriented, scene-level actionable interaction poses. We evaluated the effectiveness of our dataset by integrating it with several state-of-the-art methods for depth estimation and interaction pose prediction. Additionally, we proposed a novel modular framework that delivers superior and robust performance for generalizable articulated object manipulation. Our extensive experiments demonstrate that our dataset significantly improves the performance of depth perception and actionable interaction pose prediction in both simulation and real-world scenarios. More information and demos can be found at: https://pku-epic.github.io/GAPartManip/.
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。
