Stereo Matching with Non-Linear Diffusion

Daniel Scharstein	Richard Szeliski
Department of Computer Science Cornell University Ithaca, NY 14853, USA schar@cs.cornell.edu	Vision Technology Group Microsoft Corporation Redmond, WA 98052-6399 szeliski@microsoft.com

Abstract

One of the central problems in stereo matching (and other image registration tasks) is the selection of optimal window sizes for comparing image regions. This paper addresses this problem with some novel algorithms based on iteratively diffusing support at different disparity hypotheses, and locally controlling the amount of diffusion based on the current quality of the disparity estimate. It also develops a novel Bayesian estimation technique which significantly outperforms techniques based on area-based matching (SSD) and regular diffusion. We provide experimental results on both synthetic and real stereo image pairs.

In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'96), pages 343-350, San Francisco, CA, June 1996.