CAPT: Category-level Articulation Estimation from a Single Point Cloud
  Using Transformer
Lian Fu Ryoichi Ishikawa Yoshihiro Sato Takeshi Oishi
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.