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VocMatch: Efficient Multiview Correspondence for Structure from Motion

  • Michal Havlena
  • Konrad Schindler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8691)

Abstract

Feature matching between pairs of images is a main bottleneck of structure-from-motion computation from large, unordered image sets. We propose an efficient way to establish point correspondences between all pairs of images in a dataset, without having to test each individual pair. The principal message of this paper is that, given a sufficiently large visual vocabulary, feature matching can be cast as image indexing, subject to the additional constraints that index words must be rare in the database and unique in each image. We demonstrate that the proposed matching method, in conjunction with a standard inverted file, is 2-3 orders of magnitude faster than conventional pairwise matching. The proposed vocabulary-based matching has been integrated into a standard SfM pipeline, and delivers results similar to those of the conventional method in much less time.

Keywords

Feature matching Image clustering Structure from motion 

Supplementary material

978-3-319-10578-9_4_MOESM1_ESM.pdf (8.2 mb)
Electronic Supplementary Material (PDF 8,385 KB)
978-3-319-10578-9_4_MOESM2_ESM.wrl (12.1 mb)
Electronic Supplementary Material (WRL 12,368 KB)
978-3-319-10578-9_4_MOESM3_ESM.wrl (25.6 mb)
Electronic Supplementary Material (WRL 26,192 KB)

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Michal Havlena
    • 1
  • Konrad Schindler
    • 1
  1. 1.Institute of Geodesy and PhotogrammetryETH ZürichSwitzerland

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