  Disparity estimation is a difficult problem in stereo vision because the
correspondence technique fails in images with textureless and repetitive
regions. Recent body of work using deep convolutional neural networks (CNN)
overcomes this problem with semantics. Most CNN implementations use an
autoencoder method; stereo images are encoded, merged and finally decoded to
predict the disparity map. In this paper, we present a CNN implementation
inspired by dense networks to reduce the number of parameters. Furthermore, our
approach takes into account semantic reasoning in disparity estimation. Our
proposed network, called DenseMapNet, is compact, fast and can be trained
end-to-end. DenseMapNet requires 290k parameters only and runs at 30Hz or
faster on color stereo images in full resolution. Experimental results show
that DenseMapNet accuracy is comparable with other significantly bigger
CNN-based methods.
