  We show how to train a Convolutional Neural Network to assign a canonical
orientation to feature points given an image patch centered on the feature
point. Our method improves feature point matching upon the state-of-the art and
can be used in conjunction with any existing rotation sensitive descriptors. To
avoid the tedious and almost impossible task of finding a target orientation to
learn, we propose to use Siamese networks which implicitly find the optimal
orientations during training. We also propose a new type of activation function
for Neural Networks that generalizes the popular ReLU, maxout, and PReLU
activation functions. This novel activation performs better for our task. We
validate the effectiveness of our method extensively with four existing
datasets, including two non-planar datasets, as well as our own dataset. We
show that we outperform the state-of-the-art without the need of retraining for
each dataset.
