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.

翻译：我们提出一种面向嵌入式GPU的轻量级立体匹配系统。该系统打破了立体匹配中精度与处理速度之间的权衡关系，使嵌入式系统能够在确保实时处理的同时进一步提升匹配精度。本方法的核心思想是：基于变分自编码器构建微型神经网络，对由传统匹配方法首先生成的小尺寸粗视差图进行上采样与精细化处理。这种混合结构既能继承传统方法在计算复杂度方面的优势，又能借助神经网络确保匹配精度。在KITTI 2015基准数据集上的大量实验表明，本微型系统在提升不同算法生成的粗视差图精度方面展现出高度鲁棒性，同时在嵌入式GPU上能够实现实时运行。