Reconstruction of 3D Pose for Surfaces of Revolution from Range Data

Axial symmetry is a common property of everyday objects. Bottles, cups, cans and bowls, all usually fall in that category and can be modeled by surfaces of revolution (SOR). In this paper, we address the problem of estimating the parameters of an SOR (axis and generatrix) from range data. Although SOR reconstruction from RGB images is well studied, previous works using depth measurements are limited. We propose a formulation similar to the 3D registration problem and our solution is based on an alternating procedure that recovers the complete surface geometry, i.e. The axis and the profile curve of the SOR. We evaluate our method both quantitatively and qualitatively using four different datasets that provide depth images from a large variety of axially symmetric objects.