Ray divergence-based bundle adjustment conditioning for multi-view stereo

Authors:
Mauricio Hess-Flores;Daniel Knoblauch;Mark A. Duchaineau;Kenneth I. Joy;Falko Kuester
Affiliations:
Institute for Data Analysis and Visualization, University of California, Davis;University of California, San Diego;Lawrence Livermore National Laboratory, Livermore, CA;Institute for Data Analysis and Visualization, University of California, Davis;University of California, San Diego
Venue:
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Year:
2011

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Abstract

An algorithm that shows how ray divergence in multi-view stereo scene reconstruction can be used towards improving bundle adjustment weighting and conditioning is presented. Starting with a set of feature tracks, ray divergence when attempting to compute scene structure for each track is first obtained. Assuming accurate feature matching, ray divergence reveals mainly camera parameter estimation inaccuracies. Due to its smooth variation across neighboring feature tracks, from its histogram a set of weights can be computed that can be used in bundle adjustment to improve its convergence properties. It is proven that this novel weighting scheme results in lower reprojection errors and faster processing times than others such as image feature covariances, making it very suitable in general for applications involving multi-view pose and structure estimation.