  Stereo reconstruction from rectified images has recently been revisited
within the context of deep learning. Using a deep Convolutional Neural Network
to obtain patch-wise matching cost volumes has resulted in state of the art
stereo reconstruction on classic datasets like Middlebury and Kitti. By
introducing this cost into a classical stereo pipeline, the final results are
improved dramatically over non-learning based cost models. However these
pipelines typically include hand engineered post processing steps to
effectively regularize and clean the result. Here, we show that it is possible
to take a more holistic approach by training a fully end-to-end network which
directly includes regularization in the form of a densely connected Conditional
Random Field (CRF) that acts as a prior on inter-pixel interactions. We
demonstrate that our approach on both synthetic and real world datasets
outperforms an alternative end-to-end network and compares favorably to more
hand engineered approaches.
