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.

提出了 CenterArt，一种用于同时进行 3D 形状重建和关节体 GRASP 估计的新方法。通过 RGB-D 图像进行输入，利用编码器预测形状和关节代码，再通过解码器重建 3D 形状和估计物体的 6 个自由度的 GRASP 姿势。使用包含多个具有随机设计、纹理、照明条件和逼真深度的关节对象的逼真场景进行培训，并通过进行广泛的实验表明，CenterArt 在准确性和鲁棒性方面优于现有方法。