  To be robust to illumination changes when detecting objects in images, the
current trend is to train a Deep Network with training images captured under
many different lighting conditions. Unfortunately, creating such a training set
is very cumbersome, or sometimes even impossible, for some applications such as
3D pose estimation of specific objects, which is the application we focus on in
this paper. We therefore propose a novel illumination normalization method that
lets us learn to detect objects and estimate their 3D pose under challenging
illumination conditions from very few training samples. Our key insight is that
normalization parameters should adapt to the input image. In particular, we
realized this via a Convolutional Neural Network trained to predict the
parameters of a generalization of the Difference-of-Gaussians method. We show
that our method significantly outperforms standard normalization methods and
demonstrate it on two challenging 3D detection and pose estimation problems.
