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Uncertainty Computation in Large 3D Reconstruction

  • Michal Polic
  • Tomas Pajdla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10269)

Abstract

Many automatic methods for reconstructing camera motion and scene geometry from a large number of images evaluate the quality of the reconstruction by error propagation from measurements to the estimated parameters. Unfortunately, uncertainty propagation is computationally challenging for large scenes and hence cannot be used for large scenes in practice. We present a new algorithm for efficient uncertainty propagation which works with millions of feature points, thousands of cameras and millions of 3D points on a single computer and achieves about twenty times speedup.

Keywords

Uncertainty propagation 3D reconstruction Schur complement Information matrix Taylor expansion 

Notes

Acknowledgment

This work has been supported by the EU-H2020 project LADIO (number 731970) and Grant Agency of the CTU Prague project SGS16/230/OHK3/3T/13.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Cybernetics, Faculty of Electrical Engineering, Center for Machine PerceptionCzech Technical University in PraguePragueCzech Republic

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