Nighttime self-supervised monocular depth estimation has received increasing
attention in recent years. However, using night images for self-supervision is
unreliable because the photometric consistency assumption is usually violated
in the videos taken under complex lighting conditions. Even with domain
adaptation or photometric loss repair, performance is still limited by the poor
supervision of night images on trainable networks. In this paper, we propose a
self-supervised nighttime monocular depth estimation method that does not use
any night images during training. Our framework utilizes day images as a stable
source for self-supervision and applies physical priors (e.g., wave optics,
reflection model and read-shot noise model) to compensate for some key
day-night differences. With day-to-night data distribution compensation, our
framework can be trained in an efficient one-stage self-supervised manner.
Though no nighttime images are considered during training, qualitative and
quantitative results demonstrate that our method achieves SoTA depth estimating
results on the challenging nuScenes-Night and RobotCar-Night compared with
existing methods.

本文提出了一种自监督的夜间单目深度估计方法，不使用夜间图像进行训练，而是利用日间图像作为稳定的自监督信号，并应用物理先验来弥补昼夜差异，通过对昼夜数据分布的补偿，实现高效的单阶段自监督训练。实验结果表明，与现有方法相比，我们的方法在具有挑战性的 nuScenes-Night 和 RobotCar-Night 数据集上取得了领先的深度估计结果。