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.

通过使用 Transformer 基于类别级别的点云进行关节参数估计，本文提出了 CAPT 方法。CAPT 方法利用端到端的 Transformer 架构对单个点云中的关节参数和状态进行估计，能够准确地估计各种关节物体的关节参数和状态，并通过引入动态特征加强了表达关节物体的运动特征的方法提高了关节估计性能。此外，该文介绍了一种双重投票策略，提供了从粗到细的参数估计框架，并通过在多个类别数据集上进行实验证明了我们方法在关节估计方面优于现有方法。我们的研究为在关节物体分析中应用基于 Transformer 架构提供了有希望的解决方案。