menu drop down
menu drop down

About AI

Research achievements

You can check the results of graduate school and research.

Robust and Efficient Estimation of Relative Pose for Cameras on Selfie Sticks(Prof. Kyungdon Joo)

  • 2021
  • 01.01 - 12.31
Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence ( Early Access )
Date of Publication: 31 May 2021 
ISSN Information: Print ISSN: 0162-8828  / CD: 2160-9292 / Electronic ISSN: 1939-3539
Taking selfies has become one of the major photographic trends of our time. In this study, we focus on the selfie stick, on which a camera is mounted to take selfies. We observe that a camera on a selfie stick typically travels through a particular type of trajectory around a sphere. Based on this finding, we propose a robust, efficient, and optimal estimation method for relative camera pose between two images captured by a camera mounted on a selfie stick. We exploit the special geometric structure of camera motion constrained by a selfie stick and define this motion as spherical joint motion. Utilizing a novel parametrization and calibration scheme, we demonstrate that the pose estimation problem can be reduced to a 3-degrees of freedom (DoF) search problem, instead of a generic 6-DoF problem. This facilitates the derivation of an efficient branch-and-bound optimization method that guarantees a global optimal solution, even in the presence of outliers. Furthermore, as a simplified case of spherical joint motion, we introduce selfie motion, which has a fewer number of DoF than spherical joint motion. We validate the performance and optimality of our method on both synthetic and real-world data. Additionally, we demonstrate the applicability of the proposed method for two applications: refocusing and stylization.