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Published in:
2014 | OriginalPaper | Chapter
VocMatch: Efficient Multiview Correspondence for Structure from Motion
Authors : Michal Havlena, Konrad Schindler
Published in: Computer Vision – ECCV 2014
Publisher: Springer International Publishing
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Feature matching between pairs of images is a main bottleneck of structure-from-motion computation from large, unordered image sets. We propose an efficient way to establish point correspondences between
all
pairs of images in a dataset, without having to test each individual pair. The principal message of this paper is that, given a sufficiently large visual vocabulary,
feature matching can be cast as image indexing
, subject to the additional constraints that index words must be rare in the database and unique in each image. We demonstrate that the proposed matching method, in conjunction with a standard inverted file, is 2-3 orders of magnitude faster than conventional pairwise matching. The proposed vocabulary-based matching has been integrated into a standard SfM pipeline, and delivers results similar to those of the conventional method in much less time.