ShapeShift：基于超二次曲面的机器人抓取物体姿态估计
ShapeShift: Superquadric-based Object Pose Estimation for Robotic Grasping
AI总结：
ShapeShift提出基于超二次曲面的物体姿态估计框架，通过预测物体相对拟合原始形状的姿态，摆脱对精确三维模型的依赖，提升机器人抓取对新物体类别的泛化能力。
摘要：
物体姿态估计是机器人精确操作中的关键任务。然而，当前技术严重依赖参考三维物体，这限制了其泛化能力，并使得扩展到新物体类别成本高昂。直接姿态预测在不参考三维模型的情况下，为机器人抓取提供的信息也有限。基于关键点的方法在不依赖精确三维模型的情况下具有内在描述性，但可能缺乏一致性和准确性。为应对这些挑战，本文提出ShapeShift，一个基于超二次曲面的物体姿态估计框架，该框架预测物体相对于拟合到该物体的原始形状的姿态。所提出的框架具有内在描述性，并具备泛化到训练集之外任意几何形状的能力。
英文摘要：
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.
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。
