Abstract
This paper presents a stereo matching algorithm for obtaining dense disparity maps. Our main contribution is to introduce a new cost aggregation technique of a 3D disparity-space image data, referred to as the Optimal Path Cost Aggregation. The approach is based on the dynamic programming principle, which exactly solves one dimensional optimization problem. Furthermore, the 2D extension of the proposed technique proves an excellent approximation to the global 2D optimization problem. The effectiveness of our approach is demonstrated with several widely used synthetic and real image pairs, including ones with ground-truth value.
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Mozerov, M. (2006). An Effective Stereo Matching Algorithm with Optimal Path Cost Aggregation. In: Franke, K., Müller, KR., Nickolay, B., Schäfer, R. (eds) Pattern Recognition. DAGM 2006. Lecture Notes in Computer Science, vol 4174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861898_62
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DOI: https://doi.org/10.1007/11861898_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44412-1
Online ISBN: 978-3-540-44414-5
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