Compact Descriptor for Video Sequence Matching in the Context of Large Scale 3D Reconstruction

  • Roman Parys
  • Florian Liefers
  • Andreas Schilling
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 183)


One of the key problems in the large scale reconstruction of 3D scenes from images is how to efficiently compute image relations in large databases. Finding images depicting the same 3D geometry is the pre-requisite for camera calibration and 3D reconstruction. In this chapter we present a simple and compact descriptor that enables us to efficiently compute similarity between video sequences. In addition to providing a similarity measure, the descriptor also makes it possible to select individual video frames that match together. With our descriptors, this computation can be done in a time similar to that required by the traditional SIFT algorithm to match just two images. Using the presented descriptors, we can build a large relation graph between video streams or image sequences. This relation graph is used later in assembling a large geometric model.


Video Sequence Cluster Center Scale Invariant Feature Transform Compact Descriptor Scale Invariant Feature Transform Feature 
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 2013

Authors and Affiliations

  • Roman Parys
    • 1
  • Florian Liefers
    • 1
  • Andreas Schilling
    • 1
  1. 1.Tuebingen UniversityTübingenGermany

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