2015
CVPR
CVPR 2015
A Metric Parametrization for Trifocal Tensors With Non-Colinear Pinholes
Abstract
The trifocal tensor, which describes the relation between projections of points and lines in three views, is a fundamental entity of geometric computer vision. In this work, we investigate a new parametrization of the trifocal tensor for calibrated cameras with non-colinear pinholes obtained from a quotient Riemannian manifold. We incorporate this formulation into state-of-the art methods for optimization on manifolds, and show, through experiments in pose averaging, that it produces a meaningful way to measure distances between trifocal tensors.
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Interdisciplinary Bridge
— Computer Vision and Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— geometric computer vision
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Hot Topic Early Bird
— riemannian manifold
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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, Robotics, Security & Privacy