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2017年8月25日 · For evaluation, we present Foggy Driving, a dataset with 101 real-world images depicting foggy driving scenes, which come with ground truth annotations for semantic segmentation and object detection.

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Foggy Cityscapes is a synthetic foggy dataset which simulates fog on real scenes. Each foggy image is rendered with a clear image and depth map from Cityscapes. Thus the annotations and data split in Foggy Cityscapes are inherited from Cityscapes.

Learning scene semantics that can be well generalized to foggy conditions is important for safety-crucial applications such as autonomous driving. Existing methods need both annotated clear images and foggy images to train a curriculum domain adaptation model.

2024年10月22日 · 论文网址： Learning Generalized Segmentation for Foggy-Scenes by Bi-directional Wavelet Guidance | Proceedings of the AAAI Conference on Artificial Intelligence. 论文代码： GitHub - BiQiWHU/BWG. 英文是纯手打的！ 论文原文的summarizing and paraphrasing。 可能会出现难以避免的拼写错误和 语法错误，若有发现欢迎评论指正！ 文章偏向于笔记，谨慎食用. 1. 心得. 2. 论文逐段精读. 2.1. Abstract. 2.2. Introduction. 2.3. Related Work.

Semantic Understanding of Foggy Scenes with Purely Synthetic Data, to create Foggy Synscapes, a foggy version of the original Synscapes dataset. The pipeline computes transmittance maps, which can be combined with the original clear-weather image to obtain foggy versions of the clear-weather image.

2022年4月13日 · 图像合成雾代码实现基于论文Semantic Foggy Scene Understanding with Synthetic Data（基于合成数据的语义雾景理解），它是在Cityscapes数据集的基础上进行合成，生成了Cityscapes_foggy数据集。