Solving Viewing Graph Optimization for Simultaneous Position and Rotation Registration

TL;DR

This paper introduces an iterative method and a simultaneous approach for solving viewing graph optimization in Structure-from-Motion, improving accuracy in camera pose estimation by addressing challenges in translation and rotation integration.

Contribution

It proposes novel iterative and simultaneous methods for viewing graph optimization, enhancing accuracy in camera pose estimation in SfM.

Findings

Achieves state-of-the-art performance in experiments.

Improves accuracy in position and rotation estimation.

Effectively handles near and far camera translation observations.

Abstract

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.