SIGHT

Computer Vision is the cornerstone of outdoor applications but most algorithms are still designed to be working in clear weather. The visual artefacts caused by complex adverse weather such as rain, snow and hail were recently proved by us to be deceptive even for the best deep learning techniques. In SIGHT, we will model weather-invariant algorithms working in complex weather conditions, thus addressing the problems of understanding the visual appearance models of these weathers and making the algorithms robust to such conditions. Rather than using costly labeled data, we will leverage unsupervised learning algorithms to render physically realistic images, and tackle weather-invariant vision tasks such as semantic segmentation, object detection and long-term visual localization. Weather-invariant algorithms are crucial for any outdoor vision systems, and we expect our work to have an important impact on the deployment of autonomous driving, virtual reality, and robotics.