GPU-Accelerated Nearest Neighbor Search for 3D Registration

  • Deyuan Qiu
  • Stefan May
  • Andreas Nüchter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5815)


Nearest Neighbor Search (NNS) is employed by many computer vision algorithms. The computational complexity is large and constitutes a challenge for real-time capability. The basic problem is in rapidly processing a huge amount of data, which is often addressed by means of highly sophisticated search methods and parallelism. We show that NNS based vision algorithms like the Iterative Closest Points algorithm (ICP) can achieve real-time capability while preserving compact size and moderate energy consumption as it is needed in robotics and many other domains. The approach exploits the concept of general purpose computation on graphics processing units (GPGPU) and is compared to parallel processing on CPU. We apply this approach to the 3D scan registration problem, for which a speed-up factor of 88 compared to a sequential CPU implementation is reported.


NNS GPGPU ICP 3D registration SIMD MIMD 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Deyuan Qiu
    • 1
  • Stefan May
    • 2
  • Andreas Nüchter
    • 3
  1. 1.University of Applied Sciences Bonn-Rhein-SiegSankt AugustinGermany
  2. 2.INRIASophia-AntipolisFrance
  3. 3.Jacobs University BremenGermany

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