Sparse optical flow is widely used in various computer vision tasks, however assuming brightness consistency limits its performance in High Dynamic Range (HDR) environments. In this work, a lightweight network is used to extract illumination robust convolutional features and corners with strong invariance. Modifying the typical brightness consistency of the optical flow method to the convolutional feature consistency yields the light-robust hybrid optical flow method. The proposed network runs at 190 FPS on a commercial CPU because it uses only four convolutional layers to extract feature maps and score maps simultaneously. Since the shallow network is difficult to train directly, a deep network is designed to compute the reliability map that helps it. An end-to-end unsupervised training mode is used for both networks. To validate the proposed method, we compare corner repeatability and matching performance with origin optical flow under dynamic illumination. In addition, a more accurate visual inertial system is constructed by replacing the optical flow method in VINS-Mono. In a public HDR dataset, it reduces translation errors by 93\%. The code is publicly available at https://github.com/linyicheng1/LET-NET.

在本文中，我们使用轻量级网络提取具有强不变性的亮度鲁棒的卷积特征和角点，将光流方法的亮度一致性修改为卷积特征一致性，提出了光流的光鲁棒混合方法。使用四个卷积层同时提取特征图和得分图，该网络在商用CPU上以190 FPS运行。我们设计了一个深度网络来计算有助于训练的可靠性映射，并采用了端到端无监督训练模式。通过与原始光流在动态光照下进行角点重复性和匹配性能的比较，验证了所提出方法。此外，我们通过在VINS-Mono中替换光流方法，构建出更准确的视觉惯性系统，在公共HDR数据集上将平移误差降低了93％。

轻量级CNN网络在光流亮度一致性破裂中的应用