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.

翻译：稀疏光流被广泛应用于各类计算机视觉任务中，然而其亮度一致性假设在高动态范围（HDR）环境下限制了性能表现。本研究采用轻量级网络提取具有光照鲁棒性的卷积特征及强不变性角点，通过将传统光流方法的亮度一致性改进为卷积特征一致性，构建了光照鲁棒的混合光流方法。所提网络因仅使用四个卷积层同时提取特征图和置信度图，可在商用CPU上以190 FPS速度运行。针对浅层网络难以直接训练的问题，我们设计了用于计算可靠性映射的深度网络辅助训练。两个网络均采用端到端无监督训练模式。为验证方法有效性，我们在动态光照条件下对比了角点重复率与匹配性能。此外，通过替换VINS-Mono中的光流方法构建了更精确的视觉惯性系统，在公开HDR数据集上实现了93%的平移误差降低。代码已开源：https://github.com/linyicheng1/LET-NET