CAPT：基于Transformer的单点云类别级关节参数估计
CAPT: Category-level Articulation Estimation from a Single Point Cloud Using Transformer
AI总结：
本文提出CAPT，一种基于Transformer的端到端方法，利用运动损失和双重投票策略从单点云中实现高精度的类别级关节参数与状态估计。
摘要：
估计关节参数的能力对于机器人和计算机视觉中的各种应用至关重要。本文提出了CAPT：使用Transformer从单点云进行类别级关节参数估计。CAPT采用端到端的基于Transformer的架构，从单点云中估计关节物体的关节参数和状态。所提出的CAPT方法能够以高精度和鲁棒性准确估计各种关节物体的关节参数和状态。本文还引入了一种运动损失方法，通过强调关节物体的动态特征来提高关节参数估计性能。此外，本文提出了一种双重投票策略，为框架提供从粗到细的参数估计。在多个类别数据集上的实验结果表明，我们的方法在关节参数估计方面优于现有替代方法。我们的研究为将基于Transformer的架构应用于关节物体分析提供了一种有前景的解决方案。
英文摘要：
The ability to estimate joint parameters is essential for various applications in robotics and computer vision. In this paper, we propose CAPT: category-level articulation estimation from a point cloud using Transformer. CAPT uses an end-to-end transformer-based architecture for joint parameter and state estimation of articulated objects from a single point cloud. The proposed CAPT methods accurately estimate joint parameters and states for various articulated objects with high precision and robustness. The paper also introduces a motion loss approach, which improves articulation estimation performance by emphasizing the dynamic features of articulated objects. Additionally, the paper presents a double voting strategy to provide the framework with coarse-to-fine parameter estimation. Experimental results on several category datasets demonstrate that our methods outperform existing alternatives for articulation estimation. Our research provides a promising solution for applying Transformer-based architectures in articulated object analysis.
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。
