2015 ICCV ICCV 2015

An Efficient Minimal Solution for Multi-Camera Motion

Abstract

We propose an efficient method for estimating the motion of a multi-camera rig from a minimal set of feature correspondences. Existing methods for solving the multi-camera relative pose problem require extra correspondences, are slow to compute, and/or produce a multitude of solutions. Our solution uses a first-order approximation to relative pose in order to simplify the problem and produce an accurate estimate quickly. The solver is applicable to sequential multi-camera motion estimation and is fast enough for real-time implementation in a random sampling framework. Our experiments show that our approach is both stable and efficient on challenging test sequences.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Computer Vision and Mathematics & Optimization
🧭 Keyword Pioneer — multi-camera motion
🐣 Hot Topic Early Bird — motion estimation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio