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Comparison of Dense Stereo Using CUDA

  • Ke Zhu
  • Matthias Butenuth
  • Pablo d’Angelo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6554)

Abstract

In this paper, a local and a global dense stereo matching method, implemented using Compute Unified Device Architecture (CUDA), are presented, analyzed and compared. The purposed work shows the general strategy of the parallelization of matching methods on GPUs and the tradeoff between accuracy and run-time on current GPU hardware. Two representative and widely-used methods, the Sum of Absolute Differences (SAD) method and the Semi-Global Matching (SGM) method, are used and their results are compared using the Middlebury test sets.

Keywords

Shared Memory Global Memory Thread Block Epipolar Line Cost Aggregation 
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-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ke Zhu
    • 1
  • Matthias Butenuth
    • 2
  • Pablo d’Angelo
    • 3
  1. 1.Remote Sensing TechnologyTechnische Universit”at M”unchenGermany
  2. 2.Active Safety and Driver AssistanceIAV GmbHGermany
  3. 3.Remote Sensing Technology InstituteGerman Aerospace Center (DLR)Germany

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