This paper presents the design of an accurate rain model for the commercially-available Anyverse automotive simulation environment. The model incorporates the physical properties of rain and a process to validate the model against real rain is proposed. Due to the high computational complexity of path tracing through a particle-based model, a second more computationally efficient model is also proposed. For the second model, the rain is modeled using a combination of a particle-based model and an attenuation field. The attenuation field is fine-tuned against the particle-only model to minimize the difference between the models.
Cameras sensors are crucial for autonomous driving as they are the only sensing modality that provide measured color information of the surrounding scene. Cameras are directly exposed to external weather conditions where visibility is dramatically affected due to various reasons such as rain, ice, fog, soil, ..etc. Hence, it is crucial to detect and remove the visibility degradation caused by the harsh weather conditions. In this paper, we focus mainly on soiling degradation. We provide methods for classification of the soiled parts as well as methods for estimating the scene behind the soiled parts. A new dataset is created providing manually annotated soiled masks knows as WoodScape dataset to encourage research in that area.