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Many computer vision tasks can be formulated as labeling problems. The desired solution is often a spatially smooth labeling where label transitions are aligned with color edges of the input image. We show that such solutions can be efficiently achieved by smoothing the label costs with a very fast edge preserving filter. In this paper we propose a generic and simple framework comprising three steps: (i) constructing a cost volume (ii) fast cost volume filtering and (iii) winner-take-all label selection. Our main contribution is to show that with such a simple framework state-of-the-art results can be achieved for several computer vision applications. In particular, we achieve (i) disparity maps in real-time, whose quality exceeds those of all other fast (local) approaches on the Middlebury stereo benchmark, and (ii) optical flow fields with very fine structures as well as large displacements. To demonstrate robustness, the few parameters of our framework are set to nearly identical values for both applications. Also, competitive results for interactive image segmentation are presented. With this work, we hope to inspire other researchers to leverage this framework to other application areas.
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Christoph Rhemann
Google (United States)
Asmaa Hosni
TU Wien
Michael Bleyer
North Carolina State University
Microsoft (United States)
TU Wien
Microsoft Research (United Kingdom)
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Rhemann et al. (Wed,) studied this question.
synapsesocial.com/papers/6a09b4d5a9b5885644346239 — DOI: https://doi.org/10.1109/cvpr.2011.5995372
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