  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.
