  Precisely grasping and reconstructing articulated objects is key to enabling
general robotic manipulation. In this paper, we propose CenterArt, a novel
approach for simultaneous 3D shape reconstruction and 6-DoF grasp estimation of
articulated objects. CenterArt takes RGB-D images of the scene as input and
first predicts the shape and joint codes through an encoder. The decoder then
leverages these codes to reconstruct 3D shapes and estimate 6-DoF grasp poses
of the objects. We further develop a mechanism for generating a dataset of
6-DoF grasp ground truth poses for articulated objects. CenterArt is trained on
realistic scenes containing multiple articulated objects with randomized
designs, textures, lighting conditions, and realistic depths. We perform
extensive experiments demonstrating that CenterArt outperforms existing methods
in accuracy and robustness.
