mlsanityData to 14 Oct 2026

ShapeFit and ShapeKick for Robust, Scalable Structure from Motion

arXiv 1608.02165 ↗First posted 7 Aug 2016cs.CV cs.AI cs.NAcited by 16

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Abstract (arXiv)

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.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Aug 2016.

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Citing papers per year: 2021: 3; 2022: 0; 2023: 2; 2024: 4; 2025: 2; 2026: 5.

16 papers.

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