title: Fast cost-volume filtering for visual correspondence and beyond.
abstract: 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: 1) constructing a cost volume, 2) fast cost volume filtering, and 3) Winner-Takes-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 1) disparity maps in real time whose quality exceeds those of all other fast (local) approaches on the Middlebury stereo benchmark, and 2) optical flow fields which contain 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.
authors: Institute of Software Technology and Interactive Systems, Vienna University of Technology, Interactive Media Systems Group, Favoritenstrasse 9-11/188/2, A-1040 Vienna, Austria. asmaa@ims.tuwien.ac.at
authors: Asmaa
authors: A
authors: Hosni
authors: Christoph
authors: C
authors: Rhemann
authors: Michael
authors: M
authors: Bleyer
authors: Carsten
authors: C
authors: Rother
authors: Margrit
authors: M
authors: Gelautz
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003124:Colorimetry / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity