Category-Level Articulated Object 9D Pose Estimation via Reinforcement Learning
Liu Liu, Jianming Du, Hao Wu, Xun Yang, Zhenguang Liu, Richang Hong, Meng Wang
Abstract
Human life is populated with articulated objects. Current category-level articulated object 9D pose estimation (Articulated Object 9D Pose Estimation, ArtOPE) methods usually meet the challenges of shared object representation requirement, kinematics-agnostic pose modeling and self-occlusions. In this paper, we propose a novel framework called Articulated object 9D Pose Estimation via Reinforcement Learning (ArtPERL), which formulates the category-level ArtOPE as a reinforcement learning problem. Given a point cloud or RGB-D image input, ArtPERL firstly retrieves the part-sensitive articulated object as reference point cloud, and then introduces a joint-centric pose modeling strategy that estimates 9D pose by fitting joint states via reinforced agent training. Finally, we further propose a pose optimization that refine the predicted 9D pose considering kinematic constraints. We evaluate our ArtPERL on various datasets ranging from synthetic point cloud to real-world multi-hinged object. Experiments demonstrate the superior performance and robustness of our ArtPERL. Our work provides a new perspective on category-level articulated object 9D pose estimation and has the potential to be applied in many fields, including robotics, augmented reality, and autonomous driving.