ShapeShift: スーパークアドリックを用いたロボット把持のための物体姿勢推定
ShapeShift: Superquadric-based Object Pose Estimation for Robotic Grasping
物体にフィットさせたスーパークアドリック形状を基準に姿勢を推定することで、3Dモデルに依存せず未知形状にも汎化できるロボット把持向けの姿勢推定フレームワークを提案した。
著者: E. Zhixuan Zeng, Yuhao Chen, Alexander Wong
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
Object pose estimation is a critical task in robotics for precise object manipulation. However, current techniques heavily rely on a reference 3D object, limiting their generalizability and making it expensive to expand to new object categories. Direct pose predictions also provide limited information for robotic grasping without referencing the 3D model. Keypoint-based methods offer intrinsic descriptiveness without relying on an exact 3D model, but they may lack consistency and accuracy. To address these challenges, this paper proposes ShapeShift, a superquadric-based framework for object pose estimation that predicts the object's pose relative to a primitive shape which is fitted to the object. The proposed framework offers intrinsic descriptiveness and the ability to generalize to arbitrary geometric shapes beyond the training set.
