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Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation

  • Koichiro Yamaguchi
  • David McAllester
  • Raquel Urtasun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8693)

Abstract

In this paper we propose a slanted plane model for jointly recovering an image segmentation, a dense depth estimate as well as boundary labels (such as occlusion boundaries) from a static scene given two frames of a stereo pair captured from a moving vehicle. Towards this goal we propose a new optimization algorithm for our SLIC-like objective which preserves connecteness of image segments and exploits shape regularization in the form of boundary length. We demonstrate the performance of our approach in the challenging stereo and flow KITTI benchmarks and show superior results to the state-of-the-art. Importantly, these results can be achieved an order of magnitude faster than competing approaches.

Keywords

Flow Estimation Stereo Pair Boundary Pixel Autonomous Driving Trifocal Tensor 
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 Switzerland 2014

Authors and Affiliations

  • Koichiro Yamaguchi
    • 1
  • David McAllester
    • 2
  • Raquel Urtasun
    • 3
  1. 1.Toyota Central R&D Labs., Inc.AichiJapan
  2. 2.Toyota Technological Institute at ChicagoUSA
  3. 3.University of TorontoCanada

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