Deep stereo matching has made significant progress in recent years. However, state-of-the-art methods are based on expensive 4D cost volume, which limits their use in real-world applications. To address this issue, 3D correlation maps and iterative disparity updates have been proposed. Regarding that in real-world platforms, such as self-driving cars and robots, the Lidar is usually installed. Thus we further introduce the sparse Lidar point into the iterative updates, which alleviates the burden of network updating the disparity from zero states. Furthermore, we propose training the network in a self-supervised way so that it can be trained on any captured data for better generalization ability. Experiments and comparisons show that the presented method is effective and achieves comparable results with related methods.

翻译：近些年来,深层立体配对工作取得了显著进展。 但是,最先进的方法以昂贵的4D成本量为基础,限制了其在现实世界应用中的使用。 为了解决这个问题,提出了三维相关地图和迭代差异更新。关于现实世界平台,如自行驾驶汽车和机器人,通常安装利达尔。因此,我们进一步将稀疏的利达尔点引入迭代更新,从而减轻了更新零州差异网络的负担。此外,我们提议以自我监督的方式培训网络,以便能够在任何采集的数据方面接受培训,以提高普及能力。实验和比较表明,所提出的方法是有效的,并用相关方法取得可比的结果。