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3 March 2008 Anisotropic local high-confidence voting for accurate stereo correspondence
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Abstract
We present a local area-based, discontinuity-preserving stereo matching algorithm that achieves high quality results near depth discontinuities as well as in homogeneous regions. To address the well-known challenge of defining appropriate support windows for local stereo methods, we use the anisotropic Local Polynomial Approximation (LPA) - Intersection of Confidence Intervals (ICI) technique. It can adaptively select a nearoptimal anisotropic local neighborhood for each pixel in the image. Leveraging this robust pixel-wise shape-adaptive support window, the proposed stereo method performs a novel matching cost aggregation step and an effective disparity refinement scheme entirely within a local high-confidence voting framework. Evaluation using the benchmark Middlebury stereo database shows that our method outperforms other local stereo methods, and it is even better than some algorithms using advanced but computationally complicated global optimization techniques.
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Jiangbo Lu, Gauthier Lafruit, and Francky Catthoor "Anisotropic local high-confidence voting for accurate stereo correspondence", Proc. SPIE 6812, Image Processing: Algorithms and Systems VI, 68120J (3 March 2008); https://doi.org/10.1117/12.766481
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