Accurate Projective Reconstruction

It is possible to recover the three-dimensional structure of a scene using images taken with uncalibrated cameras and pixel correspondences. But such a reconstruction can only be computed up to a projective transformation of the 3D space. Therefore, constraints have to be added to the reconstructed data in order to get the reconstruction in the euclidean space. Such constraints arise from knowledge of the scene: location of points, geometrical constraints on lines, etc. We rst discuss here the type of constraints that have to be added then we show how they can be fed into a general framework. Experiments prove that the accuracy needed for industrial applications is reachable when measurements in the image have subpixel accuracy. Therefore, we show how a real camera can be mapped into an accurate projective camera and how accurate point detection improve the reconstruction results.