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This paper presents a new bilateral filtering method specially designed for practical stereo vision systems. Parallel algorithms are preferred in these systems due to the real-time performance requirement. Edge-preserving filters like the bilateral filter have been demonstrated to be very effective for high-quality local stereo matching. A hardware-efficient bilateral filter is thus proposed in this paper. When moved to an NVIDIA GeForce GTX 580 GPU, it can process a one megapixel \textit{color} image at around 417 frames per second. This filter can be directly used for cost aggregation required in any local stereo matching algorithm. Quantitative evaluation shows that it outperforms all the other local stereo methods both in terms of accuracy and speed on Middlebury benchmark. It ranks $12^{th}$ out of over 120 methods on Middlebury data sets, and the average runtime (including the matching cost computation, occlusion handling and post processing) is only 15 milliseconds (67 frames per second).
Kernel, Joints, Runtime, Image resolution, Image edge detection, Graphics processing units, Stereo vision, Filtering, 3D/stereo scene analysis, Computer vision

Q. Yang, "Hardware-Efficient Bilateral Filtering for Stereo Matching," in IEEE Transactions on Pattern Analysis & Machine Intelligence.
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