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Clustering as an Approach to 3D Reconstruction Problem

In: Clusters, Orders, and Trees: Methods and Applications

Author

Listed:
  • Sergey Arkhangelskiy
  • Ilya Muchnik

    (DIMACS, Rutgers University)

Abstract

Numerous applications of information technology are connected with 3D-reconstruction task. One of the important special cases is reconstruction using 3D point clouds that are collected by laser range finders and consumer devices like Microsoft Kinect. We present a novel procedure for 3D image registration that is a fundamental step in 3D objects reconstruction. This procedure reduces the task complexity by extracting small subset of potential matches which is enough for accurate registration. We obtain this subset as a result of clustering procedure applied to the broad set of potential matches, where the distance between matches reflects their consistency. Furthermore, we demonstrate the effectiveness of the proposed approach by a set of experiments in comparison with state-of-the-art techniques.

Suggested Citation

  • Sergey Arkhangelskiy & Ilya Muchnik, 2014. "Clustering as an Approach to 3D Reconstruction Problem," Springer Optimization and Its Applications, in: Fuad Aleskerov & Boris Goldengorin & Panos M. Pardalos (ed.), Clusters, Orders, and Trees: Methods and Applications, edition 127, pages 91-102, Springer.
    • Handle: RePEc:spr:spochp:978-1-4939-0742-7_6
    DOI: 10.1007/978-1-4939-0742-7_6
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