ShapeFit and ShapeKick for Robust, Scalable Structure from Motion
This provides a robust and scalable solution for computer vision researchers and practitioners working on structure from motion, though it is incremental as it builds on existing location recovery approaches.
The paper tackles the problem of location recovery from pairwise directions in structure from motion, introducing a convex program with exact recovery guarantees and robustness to adversarial outliers. The method achieves performance comparable to state-of-the-art with an order of magnitude speed-up, as demonstrated on 13 real-world image collections and simulated data.
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.