  We propose a novel approach to synthesizing images that are effective for
training object detectors. Starting from a small set of real images, our
algorithm estimates the rendering parameters required to synthesize similar
images given a coarse 3D model of the target object. These parameters can then
be reused to generate an unlimited number of training images of the object of
interest in arbitrary 3D poses, which can then be used to increase
classification performances.
  A key insight of our approach is that the synthetically generated images
should be similar to real images, not in terms of image quality, but rather in
terms of features used during the detector training. We show in the context of
drone, plane, and car detection that using such synthetically generated images
yields significantly better performances than simply perturbing real images or
even synthesizing images in such way that they look very realistic, as is often
done when only limited amounts of training data are available.
