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.
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Interdisciplinary Bridge
— Artificial Intelligence and Computer Vision and Mathematics & Optimization
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Keyword Pioneer
— multi-camera motion
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Hot Topic Early Bird
— motion estimation
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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