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3D Saliency for Finding Landmark Buildings

N. Kobyshev and H. Riemenschneider and A. Bódis-Szomorú and L. Van Gool
International Conference on 3D Vision (3DV)
October 2016


In urban environments the most interesting and effective factors for localization and navigation are landmark buildings. This paper proposes a novel method to detect such buildings that stand out, i.e. would be given the status of landmark. The method works in a fully unsupervised way, i.e. it can be applied to different cities without requiring annotation. First, salient points are detected, based on the analysis of their features as well as those found in their spatial neighborhood. Second, learning refines the points by finding connected landmark components and training a classifier to distinguish these from common building components. Third, landmark components are aggregated into complete landmark buildings. Experiments on city-scale point clouds show the viability and efficiency of our approach on various tasks.

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  author = {N. Kobyshev and H. Riemenschneider and A. Bódis-Szomorú and L. Van Gool},
  title = {3D Saliency for Finding Landmark Buildings},
  booktitle = {International Conference on 3D Vision (3DV)},
  year = {2016},
  month = {October},
  keywords = {}