A Global Matching Framework for Stereo Computation

Abstract

This paper presents a new global matching framework for stereo computation. In this framework, the second view is first predicted from the reference view using the depth information. A global match measure is then defined as the similarity function between the predicted image and the actual image. Stereo computation is converted into a search problem where the goal is to find the depth map that maximizes the global match measure. The major advantage of this framework is that the global visibility constraint is inherently enforced in the computation. This paper explores several key components of this framework including (1) three color segmentation based depth representations, (2) an incremental warping algorithm that dramatically reduces the computational complexity, and (3) scene constraints such as the smoothness constraint and the color similarity constraint. Experimental results using different types of depth representations are presented. The quality of the computed depth maps is demonstrated through image-based rendering from new viewpoints.