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3D All The Way: Semantic Segmentation of Urban Scenes From Start to End in 3D

A. Martinovic and J. Knopp and H. Riemenschneider and L. Van Gool
CVPR
June 2015

Abstract

We propose a new approach for semantic segmentation of 3D city models. Starting from an SfM reconstruction of a street-side scene, we perform classification and facade splitting purely in 3D, obviating the need for slow image-based semantic segmentation methods. We show that a properly trained pure-3D approach produces high quality labelings, with significant speed benefits (20x faster) allowing us to analyze entire streets in a matter of minutes. Additionally, if speed is not of the essence, the 3D labeling can be combined with the results of a state-of-the-art 2D classifier, further boosting the performance. Further, we propose a novel facade separation based on semantic nuances between facades. Finally, inspired by the use of architectural principles for 2D facade labeling, we propose new 3D-specific principles and an efficient optimization scheme based on an integer quadratic programming formulation.


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@InProceedings{eth_biwi_01216,
  author = {A. Martinovic and J. Knopp and H. Riemenschneider and L. Van Gool},
  title = {3D All The Way: Semantic Segmentation of Urban Scenes From Start to End in 3D},
  booktitle = {CVPR},
  year = {2015},
  month = {June},
  keywords = {}
}