  Depth maps obtained from commercially available structured-light stereo based
depth cameras, such as the Kinect, are easy to use but are affected by
significant amounts of noise. This paper is devoted to a study of the intrinsic
noise characteristics of such depth maps, i.e. the standard deviation of noise
in estimated depth varies quadratically with the distance of the object from
the depth camera. We validate this theoretical model against empirical
observations and demonstrate the utility of this noise model in three popular
applications: depth map denoising, volumetric scan merging for 3D modeling, and
identification of 3D planes in depth maps.
