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Pose estimation is one of the most important problems in computer vision. Itcan be divided in two different categories -- absolute and relative -- and mayinvolve two different types of camera models: central and non-central.State-of-the-art methods have been designed to solve separately these problems.This paper presents a unified framework that is able to solve any pose problemby alternating optimization techniques between two set of parameters, rotationand translation. In order to make this possible, it is necessary to define anobjective function that captures the problem at hand. Since the objectivefunction will depend on the rotation and translation it is not possible tosolve it as a simple minimization problem. Hence the use of AlternatingMinimization methods, in which the function will be alternatively minimizedwith respect to the rotation and the translation. We show how to use ourframework in three distinct pose problems. Our methods are then benchmarkedwith both synthetic and real data, showing their better balance betweencomputational time and accuracy.

POSEAMM: A Unified Framework for Solving Pose Problems using an Alternating Minimization Method