Single View Calibration, 3D Reconstruction and Mosaicing from Surfaces of Revolution
Image analysis and computer vision can be effectively employed to recover the three-dimensional structure of imaged objects, together with their surface properties. In this work, we address the problem of metric reconstruction and texture acquisition from a single uncalibrated view of a surface of revolution (SOR). Geometric constraints induced in the image by the symmetry properties of the SOR structure are exploited to perform self-calibration of a natural camera, 3D metric reconstruction and texture acquisition. By exploiting the analogy with the geometry of single axis motion, we demonstrate that the imaged apparent contour and the visible segments of two imaged cross sections in a single SOR view provide enough information for these tasks. Original contributions of the work are: single view self-calibration and reconstruction based on planar rectification, previously developed for planar surfaces, has been extended to deal also with the SOR class of curved surfaces; self-calibration is obtained by estimating both camera focal length (1 parameter) and principal point (2 parameters) from three independent linear constraints for the SOR fixed entities; the invariant based description of the SOR scaling function has been extended from affine to perspective projection. The solution proposed exploits both the geometric and topological properties of the transformation that relates the apparent contour to the SOR scaling function. Therefore, with this method a metric localization of the SOR occluded parts can be made, so as to cope with them correctly. For the reconstruction of textured SORs, texture acquisition is performed without requiring the estimation of external camera calibration parameters, but only using internal camera parameters obtained from self-calibration.
We also present a novel approach to obtain a mosaic image for the surface texture content of a surface of revolution (SOR) from a collection of uncalibrated views. The SOR scene constraint is used to calibrate each view and align the corresponding pictorial content into a global representation. Metric surface properties are extracted from each view by exploiting special properties of the imaged SOR geometry expressed in terms of homologies. Image alignment is achieved by projecting imaged surface elements onto a reference plane, and then registering them according to a translational motion model. This work extends previous research on calibrated scenes of right circular cylinders to the more general case of uncalibrated SOR scenes. Experimental results with images taken from the web demonstrate the effectiveness and the general applicability of the approach.