Learning to be a depth camera for close-range human capture and interaction
作者： Sean Ryan Fanello , Cem Keskin , Shahram Izadi , Pushmeet Kohli , David Kim
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摘要: 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 human-computer interaction and capture scenarios. Experiments show an …
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