GTAV-NightRain: Photometric Realistic Large-scale Dataset for Night-time Rain Streak Removal

Rain is transparent, which reflects and refracts light in the scene to the camera. In outdoor vision, rain, especially rain streaks degrade visibility and therefore need to be removed. In existing rain streak removal datasets, although density, scale, direction and intensity have been considered, transparency is not fully taken into account. This problem is particularly serious in night scenes, where the appearance of rain largely depends on the interaction with scene illuminations and changes drastically on different positions within the image. This is problematic, because unrealistic dataset causes serious domain bias. In this paper, we propose GTAV-NightRain dataset, which is a large-scale synthetic night-time rain streak removal dataset. Unlike existing datasets, by using 3D computer graphic platform (namely GTA V), we are allowed to infer the three dimensional interaction between rain and illuminations, which insures the photometric realness. Current release of the dataset contains 12,860 HD rainy images and 1,286 corresponding HD ground truth images in diversified night scenes. A systematic benchmark and analysis are provided along with the dataset to inspire further research

Rain Removal in Traffic Surveillance: Does it Matter?

Varying weather conditions, including rainfall and snowfall, are generally regarded as a challenge for computer vision algorithms. One proposed solution to the challenges induced by rain and snowfall is to artificially remove the rain from images or video using rain removal algorithms. It is the promise of these algorithms that the rain-removed image frames will improve the performance of subsequent segmentation and tracking algorithms. However, rain removal algorithms are typically evaluated on their ability to remove synthetic rain on a small subset of images. Currently, their behavior is unknown on real-world videos when integrated with a typical computer vision pipeline. In this paper, we review the existing rain removal algorithms and propose a new dataset that consists of 22 traffic surveillance sequences under a broad variety of weather conditions that all include either rain or snowfall. We propose a new evaluation protocol that evaluates the rain removal algorithms on their ability to improve the performance of subsequent segmentation, instance segmentation, and feature tracking algorithms under rain and snow. If successful, the de-rained frames of a rain removal algorithm should improve segmentation performance and increase the number of accurately tracked features. The results show that a recent single-frame-based rain removal algorithm increases the segmentation performance by 19.7% on our proposed dataset, but it eventually decreases the feature tracking performance and showed mixed results with recent instance segmentation methods. However, the best video-based rain removal algorithm improves the feature tracking accuracy by 7.72%.Comment: Published in IEEE Transactions on Intelligent Transportation System

Model-based occlusion disentanglement for image-to-image translation

Image-to-image translation is affected by entanglement phenomena, which may occur in case of target data encompassing occlusions such as raindrops, dirt, etc. Our unsupervised model-based learning disentangles scene and occlusions, while benefiting from an adversarial pipeline to regress physical parameters of the occlusion model. The experiments demonstrate our method is able to handle varying types of occlusions and generate highly realistic translations, qualitatively and quantitatively outperforming the state-of-the-art on multiple datasets.Comment: ECCV 202

Rain rendering for evaluating and improving robustness to bad weather

Rain fills the atmosphere with water particles, which breaks the common assumption that light travels unaltered from the scene to the camera. While it is well-known that rain affects computer vision algorithms, quantifying its impact is difficult. In this context, we present a rain rendering pipeline that enables the systematic evaluation of common computer vision algorithms to controlled amounts of rain. We present three different ways to add synthetic rain to existing images datasets: completely physic-based; completely data-driven; and a combination of both. The physic-based rain augmentation combines a physical particle simulator and accurate rain photometric modeling. We validate our rendering methods with a user study, demonstrating our rain is judged as much as 73% more realistic than the state-of-theart. Using our generated rain-augmented KITTI, Cityscapes, and nuScenes datasets, we conduct a thorough evaluation of object detection, semantic segmentation, and depth estimation algorithms and show that their performance decreases in degraded weather, on the order of 15% for object detection, 60% for semantic segmentation, and 6-fold increase in depth estimation error. Finetuning on our augmented synthetic data results in improvements of 21% on object detection, 37% on semantic segmentation, and 8% on depth estimation.Comment: 19 pages, 19 figures, IJCV 2020 preprint. arXiv admin note: text overlap with arXiv:1908.1033