Mining Structure Fragments for Smart Bundle Adjustment
In Proceedings British Machine Vision Conference 2014
http://dx.doi.org/10.5244/C.28.3
Abstract
Bundle 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.
Session
Machine Learning
Files
Extended Abstract (PDF, 1 page, 317K)Paper (PDF, 12 pages, 573K)
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Bibtex File
Presentation
Citation
Luca Carlone, Pablo Fernandez Alcantarilla, Han-Pang Chiu, Zsolt Kira, and Frank Dellaert. Mining Structure Fragments for Smart Bundle Adjustment. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.
BibTex
@inproceedings{BMVC.28.3
title = {Mining Structure Fragments for Smart Bundle Adjustment},
author = {Carlone, Luca and Fernandez Alcantarilla, Pablo and Chiu, Han-Pang and Kira, Zsolt and Dellaert, Frank},
year = {2014},
booktitle = {Proceedings of the British Machine Vision Conference},
publisher = {BMVA Press},
editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
doi = { http://dx.doi.org/10.5244/C.28.3 }
}