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Object pose estimation is a critical task in robotics for precise objectmanipulation. However, current techniques heavily rely on a reference 3Dobject, limiting their generalizability and making it expensive to expand tonew object categories. Direct pose predictions also provide limited informationfor robotic grasping without referencing the 3D model. Keypoint-based methodsoffer intrinsic descriptiveness without relying on an exact 3D model, but theymay lack consistency and accuracy. To address these challenges, this paperproposes ShapeShift, a superquadric-based framework for object pose estimationthat predicts the object's pose relative to a primitive shape which is fittedto the object. The proposed framework offers intrinsic descriptiveness and theability to generalize to arbitrary geometric shapes beyond the training set.

ShapeShift: Superquadric-based Object Pose Estimation for Robotic Grasping