SynthRSF-MM Expansion - A Novel Photorealistic Synthetic Dataset for Adverse Weather Condition Denoising

Published December 18, 2024 | Version v1

Dataset Open

SynthRSF-MM Expansion Dataset

SynthRSF-MM expansion:
- 13,800 additional pairs are accompanied by:
- 16-bit depth maps.
- Pixel-accurate object annotations for 41 object classes.
SynthRSF (Parts 1, 2):
- 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 (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.

Name	Size	Download all
SynthRSF-MM.zip md5:7510e0b7ec56ac5d5305ba1410df1f3b	23.4 GB	Preview Download

European Commission
RESCUER - first RESponder-Centered support toolkit for operating in adverse and infrastrUcture-less EnviRonments 101021836