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Semantically Guided Depth Upsampling

  • Nick SchneiderEmail author
  • Lukas Schneider
  • Peter Pinggera
  • Uwe Franke
  • Marc Pollefeys
  • Christoph Stiller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9796)

Abstract

We present a novel method for accurate and efficient upsampling of sparse depth data, guided by high-resolution imagery. Our approach goes beyond the use of intensity cues only and additionally exploits object boundary cues through structured edge detection and semantic scene labeling for guidance. Both cues are combined within a geodesic distance measure that allows for boundary-preserving depth interpolation while utilizing local context. We model the observed scene structure by locally planar elements and formulate the upsampling task as a global energy minimization problem. Our method determines globally consistent solutions and preserves fine details and sharp depth boundaries. In our experiments on several public datasets at different levels of application, we demonstrate superior performance of our approach over the state-of-the-art, even for very sparse measurements.

Keywords

Markov Random Field Geodesic Distance Total Generalize Variation Sparse Measurement Depth Edge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Nick Schneider
    • 1
    • 3
    Email author
  • Lukas Schneider
    • 1
    • 2
  • Peter Pinggera
    • 1
  • Uwe Franke
    • 1
  • Marc Pollefeys
    • 2
  • Christoph Stiller
    • 3
  1. 1.Environment Perception, Daimler R&DSindelfingenGermany
  2. 2.ETH ZurichZurichSwitzerland
  3. 3.Karlsruhe Institute of TechnologyKarlsruheGermany

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