Solving Viewing Graph Optimization for Simultaneous Position and Rotation Registration
This work addresses a key bottleneck in Structure-from-Motion for computer vision, offering an incremental improvement over existing methods that handle rotations and positions separately.
The paper tackles the problem of simultaneously estimating camera positions and rotations from viewing graphs, which is challenging due to translation direction ambiguities and varying camera distances. The proposed iterative method achieves state-of-the-art performance in experiments.
A viewing graph is a set of unknown camera poses, as the vertices, and the observed relative motions, as the edges. Solving the viewing graph is an essential step in a Structure-from-Motion procedure, where a set of relative motions is obtained from a collection of 2D images. Almost all methods in the literature solve for the rotations separately, through rotation averaging process, and use them for solving the positions. Obtaining positions is the challenging part because the translation observations only tell the direction of the motions. It becomes more challenging when the set of edges comprises pairwise translation observations between either near and far cameras. In this paper an iterative method is proposed that overcomes these issues. Also a method is proposed which obtains the rotations and positions simultaneously. Experimental results show the-state-of-the-art performance of the proposed methods.