Accurate automatic localization of surfaces of revolution for self-calibration and metric reconstruction

In this paper, we address the problem of the automatic metric reconstruction Surface of Revolution (SOR) from a single uncalibrated view. The apparent contour and the visible portions of the imaged SOR cross sections are extracted and classified. The harmonic homology that models the image projection of the SOR is also estimated. The special care devoted to accuracy and robustness with respect to outliers makes the approach suitable for automatic camera calibration and metric reconstruction from single uncalibrated views of a SOR. Robustness and accuracy are obtained by embedding a graph-based grouping strategy (Euclidean Minimum Spanning Tree) into an Iterative Closest Point framework for projective curve alignment at multiple scales. Classification of SOR curves is achieved through a 2-dof voting scheme based on a pencil of conics novel parametrization. The main contribution of this work is to extend the domain of automatic single view reconstruction from piecewise planar scenes to scenes including curved surfaces, thus allowing to create automatically realistic image models of man-made objects. Experimental results with real images taken from the internet are reported, and the effectiveness and limitations of the approach are discussed.