A volumetric stereo matching method: application to image-based modeling

Abstract

We formulate stereo matching as an extremal surface extraction problem. This is made possible by embedding the disparity surface inside a volume where the surface is composed of voxels with locally maximal similarity values. This formulation naturally implements the coherence principle, and allows us to incorporate most known global constraints. Time efficiency is achieved by executing the algorithm in a coarse-to-fine fashion, and only populating the full volume at the coarsest level. To make the system more practical, we present a rectification algorithm based on the fundamental matrix, avoiding full camera calibration. We present results on standard stereo pairs, and on our own data set. The results are qualitatively evaluated in terms of both the generated disparity maps and the 3-D models.