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We present a high-speed dense stereo algorithm that achieves both good quality results and very high disparity estimation throughput on the graphics processing unit (GPU). The key idea is a variable center-biased windowing approach, enabling an adaptive selection of the most suitable support patterns with varying sizes and shapes. As the fundamental construct for variable windows, a truncated separable Laplacian kernel approximation is proposed for the efficient pixel-wise weighted cost aggregation. We also present a number of critical optimization schemes to boost the real-time speed on GPUs. Our method outperforms previous GPU-based local stereo methods and even some methods using global optimization on the Middlebury stereo database. Our optimized implementation completely running on an Nvidia GeForce 7900 graphics card achieves over 605 million disparity estimations per second (Mde/s) including all the overhead, about 2.1 to 12.1 times faster than the existing GPU-based solutions.
Image Processing, 2007. ICIP 2007. IEEE International Conference on (Volume:6 )
Date of Conference: Sept. 16 2007-Oct. 19 2007