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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: 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.

Local stereo matching has recently experienced large progress by the introduction of new support aggregation schemes. These approaches estimate a pixel's support region via color segmentation. Our contribution lies in an improved method for accomplishing this segmentation. Inside a square support window, we compute the geodesic distance from all pixels to the window's center pixel. Pixels of low geodesic distance are given high support weights and therefore large influence in the matching process. In contrast to previous work, we enforce connectivity by using the geodesic distance transform. For obtaining a high support weight, a pixel must have a path to the center point along which the color does not change significantly. This connectivity property leads to improved segmentation results and consequently to improved disparity maps. The success of our geodesic approach is demonstrated on the Middlebury images. According to the Middlebury benchmark, the proposed algorithm is the top performer among local stereo methods at the current state-of-the-art.

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Adaptive support weight algorithms represent the state-of the-art in local stereo matching. Their limitation is a high computational demand, which makes them unattractive for many (real-time) applications. To our knowledge, the algo rithm proposed in this paper is the first local method which is both fast (real-time) and produces results comparable to global algorithms. A key insight is that the aggregation step of adaptive support weight algorithms is equivalent to smoothing the stereo cost volume with an edge-preserving filter. From this perspective, the original adaptive support weight algo rithm [1] applies bilateral filtering on cost volume slices, and the reason for its poor computational behavior is that bilat eral filtering is a relatively slow process. We suggest to use the recently proposed guided filter [2] to overcome this limi tation. Analogously to the bilateral filter, this filter has edge preserving properties, but can be implemented in a very fast way, which makes our stereo algorithm independent of the size of the match window. The GPU implementation of our stereo algorithm can process stereo images with a resolution of 640 x 480 pixels and a disparity range of 26 pixels at 25 fps. According to the Middlebury on-line ranking, our algo rithm achieves rank 14 out of over 100 submissions and is not only the best performing local stereo matching method, but also the best performing real-time method.