Abstract
Techniques for measuring the position and orientation of an object from corresponding images are based on the principles of epipolar geometry in the computer vision and photogrammetric fields. Contributing to their importance, many different approaches have been developed in computer vision, increasing the automation of the pure photogrammetric processes. The aim of this paper is to evaluate the main differences between photogrammetric and computer vision approaches for the pose estimation of an object from image sequences, and how these have to be considered in the choice of processing technique when using a single camera. The use of a single camera in consumer electronics has enormously increased, even though most 3D user interfaces require additional devices to sense 3D motion for their input. In this regard, using a monocular camera to determine 3D motion is unique. However, we argue that relative pose estimations from monocular image sequences have not been studied thoroughly by comparing both photogrammetry and computer vision methods. To estimate motion parameters characterized by 3D rotation and 3D translations, estimation methods developed in the computer vision and photogrammetric fields are implemented. This paper describes a mathematical motion model for the proposed approaches, by differentiating their geometric properties and estimations of the motion parameters. A precision analysis is conducted to investigate the main characteristics of the methods in both fields. The results of the comparison indicate the differences between the estimations in both fields, in terms of accuracy and the test dataset. We show that homography-based approaches are more accurate than essential-matrix or relative orientation-based approaches under noisy conditions.