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Novel Photorealistic Synthetic Dataset for Adverse Weather Condition Denoising

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SynthRSF Dataset

Overview

  • SynthRSF (Synthetic with Rain, Snow, uniform and non-uniform Fog) dataset is introduced for training and evaluating adverse weather image denoising models as well as use in object detection, semantic segmentation, and depth estimation models.
  • SynthRSF addresses a gap in synthetic datasets for adverse weather conditions, contributing significantly more photorealistic data compared to common 2D layered noise datasets, as well as additional modalities.
  • Applications include autonomous driving, surveillance, robotics, computer-assisted search-and-rescue.

Contents

  • SynthRSF:

    • 26,893 photorealistic image pairs (noisy and ground truth).
    • 14 3D scenes set in various environmental (rural/urban), contextual (indoor/outdoor) and lighting conditions (day/night).
    • Created using Unreal 5.2 engine.
  • SynthRSF-MM expansion:

    • 13,800 additional pairs are accompanied by:
    • 16-bit depth maps.
    • Pixel-accurate object annotations for 41 object classes.

SynthRSF Examples


Rain

Snow

Uniform Fog

Non-Uniform Fog

SynthRSF-MM Dataset

Overview

SynthRSF-MM is an expansion of the SynthRSF dataset, including additional modalities for a comprehensive analysis in computer vision tasks. It contains 13,800 noisy images from 14 scenes with ground truth on additional modalities.

SynthRSF-MM Example


Ground truth

Depth map

Object annotations

Fog

Rain

Snow

A sample scene from SynthRSF-MM, showing the ground truth (clear) scene, depth map, semantic segmentation stencil mask, and three noisy variants with different weather conditions. For each Ground truth image, there is one depth map, five pixel-level annotations and 8 noisy images per phenomenon.


SynthRSF Source 3D Scenes

No Description Snow Rain Fog (U) Fog (NU)
1 CitySample day 3862 10758 522 430
2 CitySample night 2695 2744 - -
3 Hillside Sample 485 412 254 308
4 AngryMesh W1 279 287 180 211
5 AngryMesh W2 400 377 171 170
6 ICVFXProdTest - - 8 14
7 Realistic Rend. - - 75 75
8 Archviz Interior - - - 30
9 Factory - - 13 16
10 Office Vol. 1 - - 45 38
11 Bedroom Vol. 1 - - 246 203
12 Kitchenette - - 3 15
13 Megascans A. A. - - 19 42
14 MeerkatDemo 250 250 51 55
Total 7971 14828 1487 1607

SynthRSF-MM Source 3D Scenes

No Description Ground Truth Depth Snow Rain Fog (NU)
1 CitySample day 226 226 1808 1808 1808
2 CitySample night 158 158 1264 1264 1264
3 Archviz Interior 32 32 - - 256
4 Factory 32 32 - - 256
5 OfficeVol1 102 102 - - 816
6 Bedroom Vol 1 22 22 - - 176
7 Kitchenette 22 22 - - 176
8 MegascansAA 20 20 - - 160
9 MegascansSnow 20 20 160 160 160
10 Desert 17 17 136 136 136
11 MeerkatDemo 21 21 168 168 168
12 ICVFXProdTest 21 21 168 168 168
13 AngryMesh W1 21 21 168 168 168
14 AngryMesh W2 21 21 168 168 168
Total 825 825 4040 4040 5720

Access

The dataset is available on Google Drive: https://drive.google.com/drive/folders/1UiXTYSIWbX2w62E-WDjxMyDGWvWDF0oX?usp=drive_link

Paper published under CC license (CC BY-NC-ND 4.0), free to use for non-commercial purposes, and requiring attribution:

Kanlis, A.; Vanian, V.; Karvarsamis, S.; Gkika, I.; Konstantoudakis, K. and Zarpalas, D. (2024). SynthRSF: A Novel Photorealistic Synthetic Dataset for Adverse Weather Condition Denoising. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, ISBN 978-989-758-679-8, ISSN 2184-4321, pages 567-574. DOI: 10.5220/0012397700003660


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