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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Discriminatively Trained Dense Surface Normal Estimation
verfasst von : L’ubor Ladický, Bernhard Zeisl, Marc Pollefeys
Erschienen in: Computer Vision – ECCV 2014
Verlag: Springer International Publishing
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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.