In many scenarios, robots encounter rotationally symmetric objects for which no known 3D model exists. To be able to grasp such objects using existing grasp point computation schemes, an estimate of their 3D-pose and shape is necessary. In this paper, we address the problem of recovering 3D-pose and shape of an unknown surface of revolution from two perspective views of known relative orientation. We propose a new algorithm for simultaneous estimation of pose and shape without making use of any cross-sections or bi-tangent points needed by other approaches. Our algorithm builds upon existing single-view SOR reconstruction approaches and couples the pose estimation and reconstruction process. Pose is optimized to minimize discrepancies between reconstructed shapes from two views. Our method works even in the presence of only one of the two apparent contours of a surface of revolution. We test our algorithm by comparing to ground-truth poses and shapes as well as performing grasping experiments. We introduce a new dataset of rotationally symmetric objects in a variety of poses and backgrounds and with a measured ground-truth pose and shape.
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