  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.
