In this work we propose the method for a rather unexplored problem of computer vision - discriminatively trained dense surface normal estimation from a single image. Our method combines contextual and segment-based cues and builds a regressor in a boosting framework by transforming the problem into the regression of coefficients of a local coding. We apply our method to two challenging data sets containing images of man-made environments, the indoor NYU2 data set and the outdoor KITTI data set. Our surface normal predictor achieves results better than initially expected, significantly outperforming state-of-the-art. © 2014 Springer International Publishing.
CITATION STYLE
Ladický, L., Zeisl, B., & Pollefeys, M. (2014). Discriminatively trained dense surface normal estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8693 LNCS, pp. 468–484). Springer Verlag. https://doi.org/10.1007/978-3-319-10602-1_31
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