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Abstract: This paper deals with a novel stereo algorithm that can generate accurate dense disparity maps in real time. The algorithm employs an effective cross-based variable support aggregation strategy within a scanline optimization framework. Rather than matching intensities directly, the use of adaptive support aggregation allows for precisely handling the weak textured regions as well as depth discontinuities. To improve the disparity results with global reasoning, we reformulate the energy function on a tree structure over the whole 2D image area, as opposed to dynamic programming of individual scanlines. By applying both intra- and inter-scanline optimizations, the algorithm reduces the typical ‘streaking’ artifact while maintaining high computational efficiency. The experimental results are evaluated on the Middlebury stereo dataset, showing that our approach is among the best for all real-time approaches. We implement the algorithm on a commodity graphics card with CUDA architecture, running at about 35 fames/s for a typical stereo pair with a resolution of 384×288 and 16 disparity levels.

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