  Stereo matching plays an indispensable part in autonomous driving, robotics
and 3D scene reconstruction. We propose a novel deep learning architecture,
which called CFP-Net, a Cross-Form Pyramid stereo matching network for
regressing disparity from a rectified pair of stereo images. The network
consists of three modules: Multi-Scale 2D local feature extraction module,
Cross-form spatial pyramid module and Multi-Scale 3D Feature Matching and
Fusion module. The Multi-Scale 2D local feature extraction module can extract
enough multi-scale features. The Cross-form spatial pyramid module aggregates
the context information in different scales and locations to form a cost
volume. Moreover, it is proved to be more effective than SPP and ASPP in
ill-posed regions. The Multi-Scale 3D feature matching and fusion module is
proved to regularize the cost volume using two parallel 3D deconvolution
structure with two different receptive fields. Our proposed method has been
evaluated on the Scene Flow and KITTI datasets. It achieves state-of-the-art
performance on the KITTI 2012 and 2015 benchmarks.
