Summary of Synfog: a Photo-realistic Synthetic Fog Dataset Based on End-to-end Imaging Simulation For Advancing Real-world Defogging in Autonomous Driving, by Yiming Xie et al.
SynFog: A Photo-realistic Synthetic Fog Dataset based on End-to-end Imaging Simulation for Advancing Real-World Defogging in Autonomous Driving
by Yiming Xie, Henglu Wei, Zhenyi Liu, Xiaoyu Wang, Xiangyang Ji
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces an end-to-end simulation pipeline for generating photo-realistic foggy images, addressing the limitations of existing synthetic datasets. The pipeline simulates the physically-based foggy scene imaging process, enabling more accurate model generalization from synthetic to real data. A new synthetic fog dataset named SynFog is presented, featuring sky light and active lighting conditions, along with varying levels of fog density. Experimental results show that models trained on SynFog outperform others in visual perception and detection accuracy when applied to real-world foggy images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a way to make fake pictures of foggy scenes look super realistic. This is important because current fake datasets aren’t very good at mimicking how cameras actually capture foggy images. The new method, called SynFog, makes more accurate and detailed foggy scenes with different levels of fog. When tested on real-world foggy images, the models trained on SynFog do much better than others. |
Keywords
* Artificial intelligence * Generalization
