Accurate depth estimation under adverse night conditions has practical impact and applications, such as on autonomous driving and rescue robots. In this work, we studied monocular depth estimation at night time in which various adverse weather, light, and different road conditions exist, with data captured in both RGB and event modalities. Event camera can better capture intensity changes by virtue of its high dynamic range (HDR), which is particularly suitable to be applied at adverse night conditions in which the amount of light is limited in the scene. Although event data can retain visual perception that conventional RGB camera may fail to capture, the lack of texture and color information of event data hinders its applicability to accurately estimate depth alone. To tackle this problem, we propose an event-vision based framework that integrates low-light enhancement for the RGB source, and exploits the complementary merits of RGB and event data. A dataset that includes paired RGB and event streams, and ground truth depth maps has been constructed. Comprehensive experiments have been conducted, and the impact of different adverse weather combinations on the performance of framework has also been investigated. The results have shown that our proposed framework can better estimate monocular depth at adverse nights than six baselines.

翻译：夜间恶劣条件下精确的深度估计对自动驾驶及救援机器人等实际应用具有重要价值。本研究针对存在多种恶劣天气、光照及道路状况的夜间环境，基于RGB与事件双模态数据开展单目深度估计研究。事件相机凭借其高动态范围（HDR）特性可更好捕捉强度变化，特别适用于光照受限的夜间恶劣场景。尽管事件数据能保留传统RGB相机难以捕获的视觉信息，但其纹理与色彩信息的缺失限制了直接进行深度估计的准确性。为解决该问题，我们提出一种基于事件视觉的框架，通过融合RGB图像的低光照增强处理，并充分利用RGB与事件数据互补优势。我们构建了包含配对RGB-事件流及真实深度图的专用数据集，开展系统性实验，研究了不同恶劣天气组合对框架性能的影响。结果表明，相较于六种基线方法，所提框架在恶劣夜间条件下能更准确地实现单目深度估计。