2016/08/07 by Thomas Goldstein, Goldstein, Thomas, Paul Hand +7 · 1 citation
Computer Science · Engineering · #68T45 #Advanced Vision and Imaging #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Mathematics #I.2.10 #I.4 #Image and Object Detection Techniques #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Robotics and Sensor-Based Localization
paper · pdf · doi:10.48550/arxiv.1608.02165
openalex publication_date 2016/08/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.