SynthRSF Dataset - Part 1 of 2

• 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-MM expansion:

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

• 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.