Learning to be a depth camera for close-range human capture and interaction

Sean Ryan Fanello, Cem Keskin, Shahram Izadi, Pushmeet Kohli, David Kim, David Sweeney, Antonio Criminisi, Jamie Shotton, Sing Bing Kang, 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 human-computer 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.