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      Computer Vision – ECCV 2016 

      Exploiting Semantic Information and Deep Matching for Optical Flow

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      Springer International Publishing

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          Are we ready for autonomous driving? The KITTI vision benchmark suite

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            Stereo processing by semiglobal matching and mutual information.

            This paper describes the Semi-Global Matching (SGM) stereo method. It uses a pixelwise, Mutual Information based matching cost for compensating radiometric differences of input images. Pixelwise matching is supported by a smoothness constraint that is usually expressed as a global cost function. SGM performs a fast approximation by pathwise optimizations from all directions. The discussion also addresses occlusion detection, subpixel refinement and multi-baseline matching. Additionally, postprocessing steps for removing outliers, recovering from specific problems of structured environments and the interpolation of gaps are presented. Finally, strategies for processing almost arbitrarily large images and fusion of disparity images using orthographic projection are proposed.A comparison on standard stereo images shows that SGM is among the currently top-ranked algorithms and is best, if subpixel accuracy is considered. The complexity is linear to the number of pixels and disparity range, which results in a runtime of just 1-2s on typical test images. An in depth evaluation of the Mutual Information based matching cost demonstrates a tolerance against a wide range of radiometric transformations. Finally, examples of reconstructions from huge aerial frame and pushbroom images demonstrate that the presented ideas are working well on practical problems.
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              Object scene flow for autonomous vehicles

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                Author and book information

                Book Chapter
                2016
                September 17 2016
                : 154-170
                10.1007/978-3-319-46466-4_10
                91669af6-5370-4739-a632-96ab8069ba6a
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