Exact Camera Location Recovery by Least Unsquared Deviations
This provides a theoretical guarantee for camera location recovery in computer vision, but it is incremental as it extends similar results from a previous algorithm.
The paper establishes that the Least Unsquared Deviations (LUD) algorithm exactly recovers camera locations with high probability under a probabilistic model with corrupted pairwise directions, building on prior work with less corruption.
We establish exact recovery for the Least Unsquared Deviations (LUD) algorithm of Ozyesil and Singer. More precisely, we show that for sufficiently many cameras with given corrupted pairwise directions, where both camera locations and pairwise directions are generated by a special probabilistic model, the LUD algorithm exactly recovers the camera locations with high probability. A similar exact recovery guarantee was established for the ShapeFit algorithm by Hand, Lee and Voroninski, but with typically less corruption.