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Throughout the evolution of the neural networks more specialized cells wereadded to the set of basic building blocks. These cells aim to improve trainingconvergence, increase the overall performance, and reduce the number ofrequired labels, all while preserving the expressive power of the universalnetwork. Inspired by the partitioning of the human visual perception systembetween the eyes and the cerebral cortex, we present TPNET, which offloadsuniversal and application-specific CNN from the bulk processing of the highresolution pixel data and performs the translation-variant image correctionwhile delegating all non-linear decision making to the network.  In this work, we explore application of TPNET to 3D perception with anarrow-baseline (0.0001-0.0025) quad stereo camera and prove that a trainednetwork provides a disparity prediction from the 2D phase correlation output bythe Tile Processor (TP) that is twice as accurate as the prediction from acarefully hand-crafted algorithm. The TP in turn reduces the dimensions of theinput features of the network and provides instrument-invariant andtranslation-invariant data, making real-time high resolution stereo 3Dperception feasible and easing the requirement to have a complete end-to-endnetwork.

See far with TPNET: a Tile Processor and a CNN Symbiosis