We present an improved three-step pipeline for the stereo matching problem
and introduce multiple novelties at each stage. We propose a new highway
network architecture for computing the matching cost at each possible
disparity, based on multilevel weighted residual shortcuts, trained with a
hybrid loss that supports multilevel comparison of image patches. A novel
post-processing step is then introduced, which employs a second deep
convolutional neural network for pooling global information from multiple
disparities. This network outputs both the image disparity map, which replaces
the conventional "winner takes all" strategy, and a confidence in the
prediction. The confidence score is achieved by training the network with a new
technique that we call the reflective loss. Lastly, the learned confidence is
employed in order to better detect outliers in the refinement step. The
proposed pipeline achieves state of the art accuracy on the largest and most
competitive stereo benchmarks, and the learned confidence is shown to
outperform all existing alternatives.

本研究提出了一种三步改进的立体匹配管道，其中包括了基于多级加权残差快捷方式的高速公路网络结构，用于计算每个可能差异的匹配成本，并引入了新的后处理步骤，旨在处理全局信息结合的图像不确定性，从而提高异常点检测的性能，并在现有技术上实现了最新的性能。