Common local stereo methods match support windows at integer-valued disparities. The implicit assumption that pixels within the support region have constant disparity does not hold for slanted surfaces and leads to a bias towards reconstructing frontoparallel surfaces. This work overcomes this bias by estimating an individual 3D plane at each pixel onto which the support region is projected. The major challenge of this approach is to find a pixel's optimal 3D plane among all possible planes whose number is infinite. We show that an ideal algorithm to solve this problem is PatchMatch [1] that we extend to find an approximate nearest neighbor according to a plane. In addition to PatchMatch's spatial propagation scheme, we propose (1) view propagation where planes are propagated among left and right views of the stereo pair and (2) temporal propagation where planes are propagated from preceding and consecutive frames of a video when doing temporal stereo. Adaptive support weights are used in matching cost aggregation to improve results at disparity borders. We also show that our slanted support windows can be used to compute a cost volume for global stereo methods, which allows for explicit treatment of occlusions and can handle large untextured regions. In the results we demonstrate that our method reconstructs highly slanted surfaces and achieves impressive disparity details with sub-pixel precision. In the Middlebury table, our method is currently top-performer among local methods and takes rank 2 among approximately 110 competitors if sub-pixel precision is considered.

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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.

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qualitative relighting results on a dataset of hundreds of real-world cellphone portraits. Because our technique can produce a 640 × 640 image in only 160 milliseconds, it may enable interactive user-facing photographic applications in the future.