MG-Grasp: スパースRGB観測によるメートルスケール幾何学的6自由度把持フレームワーク
MG-Grasp: Metric-Scale Geometric 6-DoF Grasping Framework with Sparse RGB Observations
RGB画像のみからメートルスケールの点群を再構成し、6自由度の安定した把持を生成する新しいフレームワークを提案。
詳しい要約
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著者: Kangxu Wang, Siang Chen, Chenxing Jiang, Shaojie Shen, Yixiang Dai, Guijin Wang
分類: cs.RO
原文アブストラクト
Single-view RGB-D grasp detection remains a common choice in 6-DoF robotic grasping systems, which typically requires a depth sensor. While RGB-only 6-DoF grasp methods has been studied recently, their inaccurate geometric representation is not directly suitable for physically reliable robotic manipulation, thereby hindering reliable grasp generation. To address these limitations, we propose MG-Grasp, a novel depth-free 6-DoF grasping framework that achieves high-quality object grasping. Leveraging two-view 3D foundation model with camera intrinsic/extrinsic, our method reconstructs metric-scale and multi-view consistent dense point clouds from sparse RGB images and generates stable 6-DoF grasp. Experiments on GraspNet-1Billion dataset and real world demonstrate that MG-Grasp achieves state-of-the-art (SOTA) grasp performance among RGB-based 6-DoF grasping methods.
