  We introduce gvnn, a neural network library in Torch aimed towards bridging
the gap between classic geometric computer vision and deep learning. Inspired
by the recent success of Spatial Transformer Networks, we propose several new
layers which are often used as parametric transformations on the data in
geometric computer vision. These layers can be inserted within a neural network
much in the spirit of the original spatial transformers and allow
backpropagation to enable end-to-end learning of a network involving any domain
knowledge in geometric computer vision. This opens up applications in learning
invariance to 3D geometric transformation for place recognition, end-to-end
visual odometry, depth estimation and unsupervised learning through warping
with a parametric transformation for image reconstruction error.
