Datasets:
Tasks:
Image-to-Image
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
ArXiv:
License:
Improve dataset card: Enhance metadata, add intro, and link CARLARain code (#1)
Browse files- Improve dataset card: Enhance metadata, add intro, and link CARLARain code (9f1b2ca3692cd551d79ed128eab141f491ce46a6)
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>
README.md
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license: cc-by-sa-4.0
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task_categories:
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- image-to-image
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tags:
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- rain
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---
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This is the dataset in the paper "
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* Project Page: https://kb824999404.github.io/HRIG/
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* Paper: https://arxiv.org/abs/2502.16421
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* Code: https://github.com/kb824999404/HRIG
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<table>
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</table>
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## BlenderFiles
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The Rain streak database from the paper [Rain Rendering for Evaluating and Improving Robustness to Bad Weather](https://github.com/astra-vision/rain-rendering).
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##
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When using these datasets, please cite our paper:
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```
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---
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language:
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- en
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license: cc-by-sa-4.0
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size_categories:
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- 1K<n<10K
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task_categories:
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- image-to-image
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pretty_name: 'High-resolution Rainy Image'
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tags:
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- rain
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- autonomous-driving
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- driving-simulation
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- semantic-segmentation
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- synthetic-data
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---
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The **High-resolution Rainy Image (HRI) dataset** is a synthetic dataset created through a learning-from-rendering approach, detailed in the paper [Learning from Rendering: Realistic and Controllable Extreme Rainy Image Synthesis for Autonomous Driving Simulation](https://huggingface.co/papers/2502.16421). Designed for autonomous driving simulation, HRI provides realistic and controllable extreme rainy images to enhance visual perception models. It comprises 3,200 paired rainy-clean images, along with corresponding depth and rain layer mask images, captured across three diverse scenes (lane, citystreet, and japanesestreet) at a high resolution of 2048x1024. This dataset is particularly valuable for tasks such as semantic segmentation, instance segmentation, depth estimation, and object detection in challenging weather conditions, as demonstrated in the related work with the CARLARain simulator.
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# High-resolution Rainy Image (HRI) Dataset
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This is the dataset in the paper "[Learning from Rendering: Realistic and Controllable Extreme Rainy Image Synthesis for Autonomous Driving Simulation](https://arxiv.org/abs/2502.16421)".
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* Project Page: https://kb824999404.github.io/HRIG/
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* Paper: [Learning from Rendering: Realistic and Controllable Extreme Rainy Image Synthesis for Autonomous Driving Simulation](https://arxiv.org/abs/2502.16421)
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* Code (Learning-from-Rendering): https://github.com/kb824999404/HRIG
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* Code (CARLARain): https://github.com/kb824999404/CARLARain
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<table>
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</table>
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* `clean`: background RGB images and depth images of all scenes.
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* `rainy`: rain layer images, RGB rainy images and depth rainy images of all scenes.
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* `trainset.json`: the sample lists of the training set.
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* `testset.json`: the sample lists of the test set.
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* For each sample in the training set and the test set:
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* `scene`: the scene name
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* `sequence`: the viewpoint name
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* `intensity`: the rain intensity
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* `wind`: the wind direction( all zero for the HRI dataset)
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* `background`: the path of the background RGB image
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* `depth`: the path of the background depth image
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* `rain_layer`: the path of the rain layer image
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* `rainy_depth`: the path of the rainy depth image
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* `rainy_image`: the path of the rainy RGB image
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## BlenderFiles
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The Rain streak database from the paper [Rain Rendering for Evaluating and Improving Robustness to Bad Weather](https://github.com/astra-vision/rain-rendering).
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## Citation
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When using these datasets, please cite our paper:
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```
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