Lightweight Self-Supervised Monocular Depth Estimation for All-Day Scenes Using Generative Adversarial Network

Self-supervised monocular depth estimation (MDE) has achieved performance levels comparable to supervised methods in well-lit environments. However, current methods struggle particularly with challenging nighttime scenes. Existing all-day self-supervised MDE methods often rely on specialized nighttime datasets, which require extensive data collection and annotation, adding complexity and resource demands to the training process. To overcome this limitation, we propose ADDepth, a novel lightweight all-day self-supervised MDE network. ADDepth leverages CoMoGAN to transform daytime images into nighttime scenes, thereby circumventing the need for a separate nighttime dataset. Additionally, we introduce a low-scale consistency loss to enhance depth map quality by mitigating the issue of blurred depth predictions, a common challenge caused by the reduced number of convolutional kernels in decoder layers. Our approach retains the network's lightweight design while significantly improving its generalization across different lighting conditions. Experimental results on public benchmarks validate the superiority of the proposed ADDepth. The source code is available at https://github.com/zjdzhou/ADDepth.