  To improve the robustness to rain, we present a physically-based rain
rendering pipeline for realistically inserting rain into clear weather images.
Our rendering relies on a physical particle simulator, an estimation of the
scene lighting and an accurate rain photometric modeling to augment images with
arbitrary amount of realistic rain or fog. We validate our rendering with a
user study, proving our rain is judged 40% more realistic that
state-of-the-art. Using our generated weather augmented Kitti and Cityscapes
dataset, we conduct a thorough evaluation of deep object detection and semantic
segmentation algorithms and show that their performance decreases in degraded
weather, on the order of 15% for object detection and 60% for semantic
segmentation. Furthermore, we show refining existing networks with our
augmented images improves the robustness of both object detection and semantic
segmentation algorithms. We experiment on nuScenes and measure an improvement
of 15% for object detection and 35% for semantic segmentation compared to
original rainy performance. Augmented databases and code are available on the
project page.
