Abstract
This paper introduces R-SMW, a new algorithm for stereo matching. The main aspect is the introduction of a Markov Random Field (MRF) model in the Symmetric Multiple Windows (SMW) stereo algorithm in order to obtain a non-deterministic relaxation. The SMW algorithm is an adaptive, multiple window scheme using left-right consistency to compute disparity. The MRF approach allows to combine in a single functional the disparity values coming from different windows, the left-right consistency constraint and regularization hypotheses. The optimal estimate of the disparity is obtained by minimizing an energy functional with simulated annealing. Results with both synthetic and real stereo pairs demonstrate the improvement over the original SMW algorithm, which was already proven to perform better than state-of-the-art algorithms.
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Fusiello, A., Castellani, U., Murino, V. (2001). Relaxing Symmetric Multiple Windows Stereo Using Markov Random Fields. In: Figueiredo, M., Zerubia, J., Jain, A.K. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2001. Lecture Notes in Computer Science, vol 2134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44745-8_7
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DOI: https://doi.org/10.1007/3-540-44745-8_7
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