Rainy-nuScenes: A Data Partition for Benchmarking Contaminated Vehicle Cameras through Rain

Adverse weather conditions, particularly rainfall, present substantial challenges to camera-based perception systems in Advanced Driver Assistance Systems (ADAS). Unlike human drivers, camera sensors are more vulnerable to visibility degradation caused by raindrops, which can impair essential functions such as object and lane detection. In this paper, we introduce Rainy-nuScenes, a novel extension of the widely-used nuScenes dataset, specifically designed for benchmarking water droplet detection and segmentation in automotive camera images. The dataset comprises 762 annotated images containing over 1,700 labeled water droplets, enabling a detailed analysis of their spatial distribution and geometric characteristics. We conduct a comparative study between Rainy-nuScenes and related datasets, including WoodScape, emphasizing key differences in droplet coverage, distribution patterns, and annotation strategies. Furthermore, we evaluate various camera defisheye techniques—such as linear and orthographic projections—in conjunction with U-Net [1] based convolutional neural networks (CNNs) trained on both Rainy-nuScenes and fisheye-derived WoodScape images [2]. ... mehrOur experiments show that the orthographic defisheye approach significantly improves the robustness and generalization capabilities of segmentation models. Rainy-nuScenes serves as a comprehensive benchmark for advancing ADAS algorithms in adverse weather, contributing to the development of safer and more reliable autonomous systems. The data and code are available at: https://github.com/timdietereberhardt/rainynuscenes