Abstract
Stereo Imaging is a powerful technique for determining the distance to objects using a pairs of camera spaced apart. The extremely high computational requirements of stereo vision limit application to non realtime applications where high computational calculation is available. To overcome the limitation, we reported the general strategy for parallelization of dense matching methods with CUDA (Compute Unified Device Architecture) programming.
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Hong, G. S., Hoe, W., & Kim, B. G. (2015). Performance analysis of matching cost for stereo matching with CUDA. In Lecture Notes in Electrical Engineering (Vol. 330, pp. 622–629). Springer Verlag. https://doi.org/10.1007/978-3-662-45402-2_88
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