Depth Extraction from Video Using Non-parametric Sampling

Kevin Karsch¹, Ce Liu², and Sing Bing Kang³

¹University of Illinois at Urbana-Champaign, USA

²Microsoft Research, New England, USA

³Microsoft Research, USA

Abstract. We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods fail (non-translating cameras and dynamic scenes). Our technique is applicable to single images as well as videos. For videos, we use local motion cues to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, we use a Kinect-based system to collect a large dataset containing stereoscopic videos with known depths. We show that our depth estimation technique outperforms the state-of-the-art on benchmark databases. Our technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and we demonstrate this through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.

LNCS 7576, p. 775 ff.

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