2013 ICCV ICCV 2013

A Global Linear Method for Camera Pose Registration

Abstract

We present a linear method for global camera pose registration from pairwise relative poses encoded in essential matrices. Our method minimizes an approximate geometric error to enforce the triangular relationship in camera triplets. This formulation does not suffer from the typical 'unbalanced scale' problem in linear methods relying on pairwise translation direction constraints, i.e. an algebraic error; nor the system degeneracy from collinear motion. In the case of three cameras, our method provides a good linear approximation of the trifocal tensor. It can be directly scaled up to register multiple cameras. The results obtained are accurate for point triangulation and can serve as a good initialization for final bundle adjustment. We evaluate the algorithm performance with different types of data and demonstrate its effectiveness. Our system produces good accuracy, robustness, and outperforms some well-known systems on efficiency.

🚀 Conference Pioneer — ICCV 2013
🌉 Interdisciplinary Bridge — Computer Vision and Machine Learning and Mathematics & Optimization and Robotics
📈 Trend Setter — Perception
🧭 Keyword Pioneer — trifocal tensor
🐣 Hot Topic Early Bird — camera pose
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Reinforcement Learning, Robotics