ShapeFit and ShapeKick for Robust, Scalable Structure from Motion
August 07, 2016 ยท Declared Dead ยท ๐ European Conference on Computer Vision
"No code URL or promise found in abstract"
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Authors
Thomas Goldstein, Paul Hand, Choongbum Lee, Vladislav Voroninski, Stefano Soatto
arXiv ID
1608.02165
Category
cs.CV: Computer Vision
Cross-listed
cs.AI,
math.NA,
math.OC
Citations
59
Venue
European Conference on Computer Vision
Last Checked
2 months ago
Abstract
We introduce a new method for location recovery from pair-wise directions that leverages an efficient convex program that comes with exact recovery guarantees, even in the presence of adversarial outliers. When pairwise directions represent scaled relative positions between pairs of views (estimated for instance with epipolar geometry) our method can be used for location recovery, that is the determination of relative pose up to a single unknown scale. For this task, our method yields performance comparable to the state-of-the-art with an order of magnitude speed-up. Our proposed numerical framework is flexible in that it accommodates other approaches to location recovery and can be used to speed up other methods. These properties are demonstrated by extensively testing against state-of-the-art methods for location recovery on 13 large, irregular collections of images of real scenes in addition to simulated data with ground truth.
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