CenterArt：铰接物体的联合形状重建与6-DoF抓取估计
CenterArt: Joint Shape Reconstruction and 6-DoF Grasp Estimation of Articulated Objects
AI总结：
提出CenterArt方法，通过编码器-解码器架构利用RGB-D图像同时实现铰接物体的3D形状重建与6-DoF抓取估计，并构建了相应数据集，实验证明其在准确性和鲁棒性上优于现有方法。
摘要：
精确抓取并重建铰接物体是实现通用机器人操作的关键。本文提出CenterArt，一种用于铰接物体同时进行3D形状重建和6-DoF抓取估计的新方法。CenterArt以场景的RGB-D图像作为输入，首先通过编码器预测形状和关节编码。随后，解码器利用这些编码重建3D形状并估计物体的6-DoF抓取姿态。我们进一步开发了一种为铰接物体生成6-DoF抓取真值姿态数据集的机制。CenterArt在包含多个铰接物体的真实场景中进行训练，这些场景具有随机化的设计、纹理、光照条件和真实深度。我们进行了大量实验，证明CenterArt在准确性和鲁棒性方面优于现有方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
