Near Real-Time Stereo With Adaptive Support Weight Approaches
By Asmaa Hosni, Michael Bleyer, and Margrit Gelautz
Abstract
Algorithms based on the adaptive support weight strategy currently represent the state-of-the-art in local stereo matching. Unfortunately, their good-quality results come at the price of high computation times: As opposed to standard local algorithms, incremental computation via sliding windows is not applicable for adaptive support weight windows. This paper presents a method for considerably speeding up computation times of these methods. The key idea is to exploit the adaptive support weight windows for generating an explicit over-segmentation of the reference image in a fast way. Having this explicit segmentation, we can take advantage of a modified "segmentation-based" sliding window technique, which makes run time independent of the window size. In particular, we demonstrate our transformation scheme for the geodesic stereo matcher of \cite{Hosni09} that has recently produced excellent results. Our unoptimized GPU-based implementation processes 320x240 pixel images with 26 allowed disparities at 10 frames per second and achieves rank 32 out of 74 methods in the Middlebury online benchmark.
Reference
A. Hosni, M. Bleyer, M. Gelautz: "Near Real-Time Stereo With Adaptive Support Weight Approaches"; Talk: International Symposium 3D Data Processing, Visualization and Transmission (3DPVT) 2010, Paris, France; 05-17-2010 - 05-20-2010; in: "International Symposium 3D Data Processing, Visualization and Transmission (3DPVT) 2010", (2010), 1 - 8.
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