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REal-time local stereo matching using guided image filtering

REal-time local stereo matching using guided image filtering,10.1109/ICME.2011.6012131,Asmaa Hosni,Michael BleyerI,Christoph Rhemann,Margrit Gelautz,C

REal-time local stereo matching using guided image filtering  
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Adaptive support weight algorithms represent the state-of­ the-art in local stereo matching. Their limitation is a high computational demand, which makes them unattractive for many (real-time) applications. To our knowledge, the algo­ rithm proposed in this paper is the first local method which is both fast (real-time) and produces results comparable to global algorithms. A key insight is that the aggregation step of adaptive support weight algorithms is equivalent to smoothing the stereo cost volume with an edge-preserving filter. From this perspective, the original adaptive support weight algo­ rithm [1] applies bilateral filtering on cost volume slices, and the reason for its poor computational behavior is that bilat­ eral filtering is a relatively slow process. We suggest to use the recently proposed guided filter [2] to overcome this limi­ tation. Analogously to the bilateral filter, this filter has edge­ preserving properties, but can be implemented in a very fast way, which makes our stereo algorithm independent of the size of the match window. The GPU implementation of our stereo algorithm can process stereo images with a resolution of 640 x 480 pixels and a disparity range of 26 pixels at 25 fps. According to the Middlebury on-line ranking, our algo­ rithm achieves rank 14 out of over 100 submissions and is not only the best performing local stereo matching method, but also the best performing real-time method. Index Terms-Real-time stereo matching, Local stereo, Adaptive support weights, Guided image filter.
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