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dc.contributor.authorCarlone, Luca
dc.contributor.authorAlcantarilla, Pablo Fernandez
dc.contributor.authorChiu, Han-Pang
dc.contributor.authorKira, Zsolt
dc.contributor.authorDellaert, Frank
dc.date.accessioned2015-08-10T19:16:40Z
dc.date.available2015-08-10T19:16:40Z
dc.date.issued2014-09
dc.identifier.citationCarlone, L., Alcantarilla, P., Chiu, H., Kira, Z., & Dellaert, F. (2014). "Mining Structure Fragments for Smart Bundle Adjustment", in Proceedings of British Machine Vision Conference (BMVC), 2014.en_US
dc.identifier.urihttp://hdl.handle.net/1853/53716
dc.description© 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.en_US
dc.description.abstractBundle Adjustment (BA) can be seen as an inference process over a factor graph. From this perspective, the Schur complement trick can be interpreted as an ordering choice for elimination. The elimination of a single point in the BA graph induces a factor over the set of cameras observing that point. This factor has a very low information content (a point observation enforces a low-rank constraint on the cameras). In this work we show that, when using conjugate gradient solvers, there is a computational advantage in “grouping” factors corresponding to sets of points (fragments) that are co-visible by the same set of cameras. Intuitively, we collapse many factors with low information content into a single factor that imposes a high-rank constraint among the cameras. We provide a grounded way to group factors: the selection of points that are co-observed by the same camera patterns is a data mining problem, and standard tools for frequent pattern mining can be applied to reveal the structure of BA graphs. We demonstrate the computational advantage of grouping in large BA problems and we show that it enables a consistent reduction of BA time with respect to state-of-the-art solvers (Ceres [1]).en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectBundle adjustmenten_US
dc.subjectFactor graphen_US
dc.subjectFrequent pattern miningen_US
dc.titleMining Structure Fragments for Smart Bundle Adjustmenten_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Robotics and Intelligent Machinesen_US
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computingen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Interactive Computingen_US
dc.contributor.corporatenameGeorgia Tech Research Instituteen_US
dc.contributor.corporatenameSRI International. Division of Information and Computing Sciencesen_US
dc.contributor.corporatenameToshiba Research Europe. Cambridge Research Laboratoryen_US
dc.publisher.originalBritish Machine Vision Association
dc.embargo.termsnullen_US


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