  We describe a non-parametric, "example-based" method for estimating the depth
of an object, viewed in a single photo. Our method consults a database of
example 3D geometries, searching for those which look similar to the object in
the photo. The known depths of the selected database objects act as shape
priors which constrain the process of estimating the object's depth. We show
how this process can be performed by optimizing a well defined target
likelihood function, via a hard-EM procedure. We address the problem of
representing the (possibly infinite) variability of viewing conditions with a
finite (and often very small) example set, by proposing an on-the-fly example
update scheme. We further demonstrate the importance of non-stationarity in
avoiding misleading examples when estimating structured shapes. We evaluate our
method and present both qualitative as well as quantitative results for
challenging object classes. Finally, we show how this same technique may be
readily applied to a number of related problems. These include the novel task
of estimating the occluded depth of an object's backside and the task of
tailoring custom fitting image-maps for input depths.
