Self-supervised depth estimation draws a lot of attention recently as it can promote the 3D sensing capabilities of self-driving vehicles. However, it intrinsically relies upon the photometric consistency assumption, which hardly holds during nighttime. Although various supervised nighttime image enhancement methods have been proposed, their generalization performance in challenging driving scenarios is not satisfactory. To this end, we propose the first method that jointly learns a nighttime image enhancer and a depth estimator, without using ground truth for either task. Our method tightly entangles two self-supervised tasks using a newly proposed uncertain pixel masking strategy. This strategy originates from the observation that nighttime images not only suffer from underexposed regions but also from overexposed regions. By fitting a bridge-shaped curve to the illumination map distribution, both regions are suppressed and two tasks are bridged naturally. We benchmark the method on two established datasets

自监督深度估计最近引起了人们的极大关注 推广自动驾驶车辆的3D传感能力。然而，它 本质上依赖于光度学一致性假设，这几乎不是 在夜间举行。尽管各种监督的夜间图像增强 已有方法被提出，它们的推广性能在挑战 驾驶场景并不令人满意。为此，我们提出了第一种方法 共同学习夜间图像增强器和深度估计器，而不是 在任何一项任务中都要使用基本事实。我们的方法紧紧地缠绕在两个 使用新提出的不确定像素掩蔽策略的自监督任务。 这一策略起源于观察到的夜间图像不仅 不仅受到曝光不足区域的影响，而且还受到过度曝光区域的影响。通过拟合 一条桥形曲线向光照贴图分布，两个区域均为 被压制和两项任务自然地联系在一起。我们根据两个基准对该方法进行基准测试 已建立的数据集