  To reduce the human efforts in neural network design, Neural Architecture
Search (NAS) has been applied with remarkable success to various high-level
vision tasks such as classification and semantic segmentation. The underlying
idea for the NAS algorithm is straightforward, namely, to enable the network
the ability to choose among a set of operations (e.g., convolution with
different filter sizes), one is able to find an optimal architecture that is
better adapted to the problem at hand. However, so far the success of NAS has
not been enjoyed by low-level geometric vision tasks such as stereo matching.
This is partly due to the fact that state-of-the-art deep stereo matching
networks, designed by humans, are already sheer in size. Directly applying the
NAS to such massive structures is computationally prohibitive based on the
currently available mainstream computing resources. In this paper, we propose
the first end-to-end hierarchical NAS framework for deep stereo matching by
incorporating task-specific human knowledge into the neural architecture search
framework. Specifically, following the gold standard pipeline for deep stereo
matching (i.e., feature extraction -- feature volume construction and dense
matching), we optimize the architectures of the entire pipeline jointly.
Extensive experiments show that our searched network outperforms all
state-of-the-art deep stereo matching architectures and is ranked at the top 1
accuracy on KITTI stereo 2012, 2015 and Middlebury benchmarks, as well as the
top 1 on SceneFlow dataset with a substantial improvement on the size of the
network and the speed of inference. The code is available at
https://github.com/XuelianCheng/LEAStereo.
