Recent advanced studies have spent considerable human efforts on optimizing
network architectures for stereo matching but hardly achieved both high
accuracy and fast inference speed. To ease the workload in network design,
neural architecture search (NAS) has been applied with great success to various
sparse prediction tasks, such as image classification and object detection.
However, existing NAS studies on the dense prediction task, especially stereo
matching, still cannot be efficiently and effectively deployed on devices of
different computing capabilities. To this end, we propose to train an elastic
and accurate network for stereo matching (EASNet) that supports various 3D
architectural settings on devices with different computing capabilities. Given
the deployment latency constraint on the target device, we can quickly extract
a sub-network from the full EASNet without additional training while the
accuracy of the sub-network can still be maintained. Extensive experiments show
that our EASNet outperforms both state-of-the-art human-designed and NAS-based
architectures on Scene Flow and MPI Sintel datasets in terms of model accuracy
and inference speed. Particularly, deployed on an inference GPU, EASNet
achieves a new SOTA 0.73 EPE on the Scene Flow dataset with 100 ms, which is
4.5$\times$ faster than LEAStereo with a better quality model.

提出了一种适用于不同计算环境下实现不同 3D 架构的弹性和准确的立体匹配网络 (EASNet)，并在 Scene Flow 和 MPI Sintel 数据集上证明其优于当前最先进的面向任务过程的 SOTA 架构。