A study of fast, robust stereo-matching algorithms
Wenxian Hong · 2010
Stereo matching is an actively researched topic in computer vision. The goal is to recover quantitative depth information from a set of input images, based on the vi-sual disparity between corresponding points. This thesis investigates several fast and robust techniques for the task. First, multiple stereo pairs with different baselines may be meaningfully combined to improve the accuracy of depth estimates. In multi-baseline stereo, individual pairwise similarity measures are aggregated into a single evaluation function. We propose the novel product-of-error-correlation function as an effective example of this aggregate function. By imposing a common variable, inverse distance, across all stereo pairs, the correct values are reinforced while false matches are eliminated. Next, in a two-view stereo context, the depth estimates may also be made more robust by accounting for foreshortening effects. We propose an algorithm that allows a matching window to locally deform according to the surface orientation of the imaged point. The algorithm then performs correlation in multiple dimensions to simultaneously determine the most probable depth and tilt. The 2D surface ori-