With the development of deep neural network generative models in recent years, significant progress has been made in the research of depth estimation in lane scenes. However, current research achievements are mainly focused on clear daytime scenarios. In complex rainy environments, the influence of rain streaks and local fog effects often leads to erroneous increases in the overall depth estimation values in images. Moreover, these natural factors can introduce disturbances to the accurate prediction of depth boundaries in images. In this paper, we investigate lane depth estimation in complex rainy environments. Based on the concept of convolutional kernel prediction, we propose a dual-layer pixel-wise convolutional kernel prediction network trained on offline data. By predicting two sets of independent convolutional kernels for the target image, we restore the depth information loss caused by complex environmental factors and address the issue of rain streak artifacts generated by a single convolutional kernel set. Furthermore, considering the lack of real rainy lane data currently available, we introduce an image synthesis algorithm, RCFLane, which comprehensively considers the darkening of the environment due to rainfall and local fog effects. We create a synthetic dataset containing 820 experimental images, which we refer to as RainKITTI, on the commonly used depth estimation dataset KITTI. Extensive experiments demonstrate that our proposed depth estimation framework achieves favorable results in highly complex lane rainy environments.

翻译：近年来，随着深度神经网络生成模型的发展，车道场景深度估计研究取得了显著进展。然而，当前研究成果主要集中在晴朗白天的场景中。在复杂雨天环境下，雨纹和局部雾气效应的影响往往导致图像整体深度估计值错误增大。此外，这些自然因素还会对图像深度边界的准确预测造成干扰。本文研究了复杂雨天环境下的车道深度估计问题。基于卷积核预测的思想，我们提出了一种双层逐像素卷积核预测网络，该网络使用离线数据进行训练。通过为目标图像预测两组独立的卷积核，我们恢复了因复杂环境因素导致的深度信息损失，并解决了单组卷积核生成的雨纹伪影问题。此外，考虑到目前缺乏真实的雨天车道数据，我们引入了一种图像合成算法RCFLane，该算法综合考量了降雨导致的环境变暗和局部雾气效应。我们在常用的深度估计数据集KITTI上创建了一个包含820张实验图像的合成数据集，称为RainKITTI。大量实验表明，我们提出的深度估计框架在高度复杂的车道雨天环境中取得了良好的效果。