  In recent years, numerous real-time stereo matching methods have been
introduced, but they often lack accuracy. These methods attempt to improve
accuracy by introducing new modules or integrating traditional methods.
However, the improvements are only modest. In this paper, we propose a novel
strategy by incorporating knowledge distillation and model pruning to overcome
the inherent trade-off between speed and accuracy. As a result, we obtained a
model that maintains real-time performance while delivering high accuracy on
edge devices. Our proposed method involves three key steps. Firstly, we review
state-of-the-art methods and design our lightweight model by removing redundant
modules from those efficient models through a comparison of their
contributions. Next, we leverage the efficient model as the teacher to distill
knowledge into the lightweight model. Finally, we systematically prune the
lightweight model to obtain the final model. Through extensive experiments
conducted on two widely-used benchmarks, Sceneflow and KITTI, we perform
ablation studies to analyze the effectiveness of each module and present our
state-of-the-art results.
