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Parallel Processing of Range Data Merging

  • Ryusuke Sagawa
  • Ko Nishino
  • Mark D. Wheeler
  • Katsushi Ikeuchi

This chapter describes a volumetric view-merging algorithm that generates a consensus surface of an object from its range images. Our original method merges a set of range images into a volumetric implicit-surface representation, which is converted to a surface mesh by using a variant of the marching-cubes algorithm. We propose a method that increases the computation and memory efficiency for computing signed distances and the method of parallel computing on a PC cluster. Since our method permits a reduction in the data amount allocated in memory, the closest point is searched efficiently; this allows us to increase the number of parallel traversals and to reduce the computation time.

In this chapter, we describe the following two algorithms which are complementary in terms of the efficiency of CPUs and memory usage: distributed allocation of range data and parallel traversal of partial octrees. By adjusting them according to the system specifications, we can build the model efficiently by a PC cluster. We have implemented this system and evaluated its performance.

Keywords

Parallel Processing Close Point Range Data Signed Distance Range Image 
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 Science+Business Media, LLC 2008

Authors and Affiliations

  • Ryusuke Sagawa
  • Ko Nishino
  • Mark D. Wheeler
  • Katsushi Ikeuchi
    • 1
  1. 1.Institute of Industrial ScienceThe University of TokyoMeguro-kuJapan

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