Learning to be a Depth Camera for Close-Range Human Capture and Interaction [Best Demo Honorable Mention Award] - Fanello, Sean; Keskin, Cem; Izadi, Shahram; Kohli, Pushmeet; Kim, David; Sweeney, David; Criminisi, Antonio; Shotton, Jamie; Kang, Sing Bing; Paek, Tim (ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH 2014, 2014)
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.