{"id":166980,"date":"2014-07-01T00:00:00","date_gmt":"2014-07-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/learning-to-be-a-depth-camera-for-close-range-human-capture-and-interaction\/"},"modified":"2022-12-21T02:23:53","modified_gmt":"2022-12-21T10:23:53","slug":"learning-to-be-a-depth-camera-for-close-range-human-capture-and-interaction","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/learning-to-be-a-depth-camera-for-close-range-human-capture-and-interaction\/","title":{"rendered":"Learning to be a Depth Camera for Close-Range Human Capture and Interaction [Best Demo Honorable Mention Award]"},"content":{"rendered":"<div class=\"asset-content\">\n<p>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.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>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. 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