Design of an improved framework for stratified 3D reconstruction from a pair of images, with reduced ambiguity

Reconstruction of three dimensional (3D) object structure from multiple images is a fundamental problem in computational vision. Many applications in computer vision require the use of structure information of 3D objects. The objective of this work is to develop a stable method of 3D reconstruction of an object, which works without the availability of camera parameters, once the plane at infinity is obtained using the approximate scene information. First, a framework has been designed based on a modification of the auto-calibration procedure for 3D structure computation using singular value decomposition. In the second part of the work, ambiguities present at the various stages of 3D reconstruction have been analysed. Error norms have been proposed, and studied to quantify the ambiguity in the reconstruction process. We attempt to analyse the effect of pose difference between camera views and focal length parameters on the reconstruction process, using experimentation with simulated and real-world data.