  Throughout the evolution of the neural networks more specialized cells were
added to the set of basic building blocks. These cells aim to improve training
convergence, increase the overall performance, and reduce the number of
required labels, all while preserving the expressive power of the universal
network. Inspired by the partitioning of the human visual perception system
between the eyes and the cerebral cortex, we present TPNET, which offloads
universal and application-specific CNN from the bulk processing of the high
resolution pixel data and performs the translation-variant image correction
while delegating all non-linear decision making to the network.
  In this work, we explore application of TPNET to 3D perception with a
narrow-baseline (0.0001-0.0025) quad stereo camera and prove that a trained
network provides a disparity prediction from the 2D phase correlation output by
the Tile Processor (TP) that is twice as accurate as the prediction from a
carefully hand-crafted algorithm. The TP in turn reduces the dimensions of the
input features of the network and provides instrument-invariant and
translation-invariant data, making real-time high resolution stereo 3D
perception feasible and easing the requirement to have a complete end-to-end
network.

Computer Vision and Pattern Recognition