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Learning to be a Depth Camera for Close-Range Human Capture and Interaction

Sean Fanello, Cem Keskin, Shahram Izadi, Pushmeet Kohli, David Kim, David Sweeney, Antonio Criminisi, Jamie Shotton, Sing Bing Kang, and Tim Paek

Abstract

We present a machine learning technique for estimating absolute, per-pixel depth using any conventional monocular 2D camera, with minor hardware modifications. Our approach targets close-range human capture and interaction where dense 3D estimation of hands and faces is desired. We use hybrid classification-regression forests to learn how to map from near infrared intensity images to absolute, metric depth in real-time. We demonstrate a variety of humancomputer interaction and capture scenarios. Experiments show an accuracy that outperforms a conventional light fall-off baseline, and is comparable to high-quality consumer depth cameras, but with a dramatically reduced cost, power consumption, and form-factor.

Details

Publication typeInproceedings
Published inJournal ACM Transactions on Graphics (TOG)
URLhttp://dl.acm.org/citation.cfm?doid=2601097.2601223
PublisherACM – Association for Computing Machinery
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