2015
CVPR
CVPR 2015
Fast Bilateral-Space Stereo for Synthetic Defocus
Abstract
Given a stereo pair it is possible to recover a depth map and use that depth to render a synthetically defocused image. Though stereo algorithms are well-studied, rarely are those algorithms considered solely in the context of producing these defocused renderings. In this paper we present a technique for efficiently producing disparity maps using a novel optimization framework in which inference is performed in "bilateral-space". Our approach produces higher-quality "defocus" results than other stereo algorithms while also being 10-100 times faster than comparable techniques.
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Interdisciplinary Bridge
— Computer Vision and Mathematics & Optimization
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Keyword Pioneer
— bilateral space
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio