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The problem of detecting the visible and hidden areas of a topographic surface from a given viewpoint is well known, and has applications in GIS and other fields. The computational geometry literature is rich with solutions based on a polygonal representation of the surface. This work confronts the problem by working directly on the Digital Elevation Map(More)
We present an improved three-step pipeline for the stereo matching problem and introduce multiple novelties at each stage. We propose a new highway network architecture for computing the matching cost at each possible disparity, based on multilevel weighted residual shortcuts, trained with a hybrid loss that supports multilevel comparison of image patches.(More)
Previous architectures in the literature for computing the matching cost report no benefit from using color information [3, 1]. In our experiments we observed that after deepening our network, the use of the three input channels contributes to the accuracy of the disparity prediction, especially around areas of delicate color differences between the object(More)
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