  In deep learning-based local stereo matching methods, larger image patches
usually bring better stereo matching accuracy. However, it is unrealistic to
increase the size of the image patch size without restriction. Arbitrarily
extending the patch size will change the local stereo matching method into the
global stereo matching method, and the matching accuracy will be saturated. We
simplified the existing Siamese convolutional network by reducing the number of
network parameters and propose an efficient CNN based structure, namely
Adaptive Deconvolution-based disparity matching Net (ADSM net) by adding
deconvolution layers to learn how to enlarge the size of input feature map for
the following convolution layers. Experimental results on the KITTI 2012 and
2015 datasets demonstrate that the proposed method can achieve a good trade-off
between accuracy and complexity.
