  Efficient real-time disparity estimation is critical for the application of
stereo vision systems in various areas. Recently, stereo network based on
coarse-to-fine method has largely relieved the memory constraints and speed
limitations of large-scale network models. Nevertheless, all of the previous
coarse-to-fine designs employ constant offsets and three or more stages to
progressively refine the coarse disparity map, still resulting in
unsatisfactory computation accuracy and inference time when deployed on mobile
devices. This paper claims that the coarse matching errors can be corrected
efficiently with fewer stages as long as more accurate disparity candidates can
be provided. Therefore, we propose a dynamic offset prediction module to meet
different correction requirements of diverse objects and design an efficient
two-stage framework. Besides, we propose a disparity-independent convolution to
further improve the performance since it is more consistent with the local
statistical characteristics of the compact cost volume. The evaluation results
on multiple datasets and platforms clearly demonstrate that, the proposed
network outperforms the state-of-the-art lightweight models especially for
mobile devices in terms of accuracy and speed. Code will be made available.
