• DOI:10.1109/IVS.2016.7535373
Corpus ID: 43887967

Semantic Stixels: Depth is not enough

@article{Schneider2016SemanticSD,
  title={Semantic Stixels: Depth is not enough},
  author={L. Schneider and Marius Cordts and Timo Rehfeld and D. Pfeiffer and M. Enzweiler and U. Franke and M. Pollefeys and S. Roth},
  journal={2016 IEEE Intelligent Vehicles Symposium (IV)},
  year={2016},
  pages={110-117}
}

L. Schneider, Marius Cordts, +5 authors S. Roth

Published 2016

Computer Science

2016 IEEE Intelligent Vehicles Symposium (IV)

In this paper we present Semantic Stixels, a novel vision-based scene model geared towards automated driving. Our model jointly infers the geometric and semantic layout of a scene and provides a compact yet rich abstraction of both cues using Stixels as primitive elements. Geometric information is incorporated into our model in terms of pixel-level disparity maps derived from stereo vision. For semantics, we leverage a modern deep learning-based scene labeling approach that provides an object… CONTINUE READING

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