Towards Robust Category-level Articulation Pose Estimation via Integrated Differentiable Rendering

Abstract

Accurate object pose estimation is crucial for embodied intelligence tasks such as manipulation, grasping, and human-robot interaction. However, due to the inherent characteristics of articulated objects, such as kinematic constraints and self-occlusion, pose estimation for articulated objects has remained a significant challenge. To address these issues, this paper proposes CAPED, an end-to-end robust Category-level Articulated object Pose Estimator integrated differentiable rendering. Given partial point cloud as input, CAPED outputs the per-part 6D pose for articulation. Specifically, with the proposed joint-centric modeling manner, CAPED firstly estimates the pose for the free part. Afterward, we canonicalize the input point cloud to estimate constrained parts’ poses by predicting the joint parameters and states as replacements. For further refinement, we propose a differentiable rendering scheme for pose optimization. Evaluations of the ArtImage and RobotArm datasets demonstrate that CAPED exhibits outstanding effectiveness and generalization in tasks ranging from synthetic data to real-world scenarios. We will publicly release the code.