CenterArt: Joint Shape Reconstruction and 6-DoF Grasp Estimation of Articulated Objects

TL;DR

CenterArt is a novel method that simultaneously reconstructs 3D shapes and estimates 6-DoF grasps for articulated objects from RGB-D images, improving robotic manipulation capabilities.

Contribution

It introduces a joint shape reconstruction and grasp estimation framework along with a new dataset for articulated objects, advancing the state of robotic manipulation.

Findings

Outperforms existing methods in accuracy

Demonstrates robustness in realistic scenes

Effective in multi-object scenarios

Abstract

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.