Rainy screens: Collecting rainy datasets, indoors
This addresses the cumbersome data acquisition issue for robotics in adverse weather conditions, though it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of acquiring diverse rainy image datasets for robotics by proposing a method to generate rainy images from clear ground-truth images using a high-resolution screen, resulting in models that generalize well with quantitative improvements in image reconstruction and semantic segmentation.
Acquisition of data with adverse conditions in robotics is a cumbersome task due to the difficulty in guaranteeing proper ground truth and synchronising with desired weather conditions. In this paper, we present a simple method - recording a high resolution screen - for generating diverse rainy images from existing clear ground-truth images that is domain- and source-agnostic, simple and scales up. This setup allows us to leverage the diversity of existing datasets with auxiliary task ground-truth data, such as semantic segmentation, object positions etc. We generate rainy images with real adherent droplets and rain streaks based on Cityscapes and BDD, and train a de-raining model. We present quantitative results for image reconstruction and semantic segmentation, and qualitative results for an out-of-sample domain, showing that models trained with our data generalize well.