  We present a lightweight system for stereo matching through embedded GPUs. It
breaks the trade-off between accuracy and processing speed in stereo matching,
enabling our embedded system to further improve the matching accuracy while
ensuring real-time processing. The main idea of our method is to construct a
tiny neural network based on variational auto-encoder (VAE) to upsample and
refinement a small size of coarse disparity map, which is first generated by a
traditional matching method. The proposed hybrid structure cannot only bring
the advantage of traditional methods in terms of computational complexity, but
also ensure the matching accuracy under the impact of neural network. Extensive
experiments on the KITTI 2015 benchmark demonstrate that our tiny system
exhibits high robustness in improving the accuracy of the coarse disparity maps
generated by different algorithms, while also running in real-time on embedded
GPUs.
