Real-time stereo correspondence using a truncated separable laplacian kernel approximation on graphics hardware

We present a novel real-time stereo algorithm that achieves both good quality results and very high disparity estimation throughput on the graphics processing unit (GPU). As the key idea of this paper, a truncated separable approximation to an isotropic Laplacian kernel is proposed. This truncated 2D Laplacian kernel variant combines the advantages of large support windows and shiftable windows, while support-weights on geometric proximity can still be appropriately applied to each pixel in truncated support windows. Our method outperforms previous GPU-based local stereo methods and even some methods using global optimization on the benchmark Middlebury stereo database. Because of its separable and regular property, the proposed kernel can be very efficiently implemented on GPUs. Our optimized implementation completely running on an Nvidia GeForce 7900 graphics card achieves over 668 million disparity estimations per second (Mde/s) including all the overhead, about 2.3 to 13.4 times faster than the existing GPU-based solutions.