# SynFog: A Photo-realistic Synthetic Fog Dataset based on End-to-end Imaging Simulation for Advancing Real-World Defogging in Autonomous Driving

arXiv: https://arxiv.org/abs/2403.17094 · HTML: https://mlsanity.com/paper/2403.17094/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 53 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Builds on (most cited first, 22 of 22)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Benchmarking Single Image Dehazing and Beyond](https://mlsanity.com/paper/1712.04143/) (2017)
- [Semantic Foggy Scene Understanding with Synthetic Data](https://mlsanity.com/paper/1708.07819/) (2017)
- [Virtual Worlds as Proxy for Multi-Object Tracking Analysis](https://mlsanity.com/paper/1605.06457/) (2016)
- [FFA-Net: Feature Fusion Attention Network for Single Image Dehazing](https://mlsanity.com/paper/1911.07559/) (2019)
- [Vision Transformers for Single Image Dehazing](https://mlsanity.com/paper/2204.03883/) (2022)
- [Multi-Scale Boosted Dehazing Network with Dense Feature Fusion](https://mlsanity.com/paper/2004.13388/) (2020)
- [GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing](https://mlsanity.com/paper/1908.03245/) (2019)
- [Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields](https://mlsanity.com/paper/1502.07411/) (2015)
- [SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation](https://mlsanity.com/paper/2206.08367/) (2022)
- [NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images](https://mlsanity.com/paper/2005.03560/) (2020)
- [Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images](https://mlsanity.com/paper/1904.02904/) (2019)
- [O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images](https://mlsanity.com/paper/1804.05101/) (2018)
- [Nighttime Dehazing with a Synthetic Benchmark](https://mlsanity.com/paper/2008.03864/) (2020)
- [Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding](https://mlsanity.com/paper/1901.01415/) (2019)
- [Semantic Understanding of Foggy Scenes with Purely Synthetic Data](https://mlsanity.com/paper/1910.03997/) (2019)
- [Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications](https://mlsanity.com/paper/1710.06270/) (2017)
- [A Multi-purpose Realistic Haze Benchmark with Quantifiable Haze Levels and Ground Truth](https://mlsanity.com/paper/2206.06427/) (2022)
- [Soft Prototyping Camera Designs for Car Detection Based on a Convolutional Neural Network](https://mlsanity.com/paper/1910.10916/) (2019)
- [A system for generating complex physically accurate sensor images for automotive applications](https://mlsanity.com/paper/1902.04258/) (2019)
- [Realistic Large-Scale Fine-Depth Dehazing Dataset from 3D Videos](https://mlsanity.com/paper/2004.08554/) (2020)

## Built on by (most cited first, 9 of 9)

- [HazyDet: Open-Source Benchmark for Drone-View Object Detection with Depth-Cues in Hazy Scenes](https://mlsanity.com/paper/2409.19833/) (2024)
- [RALAD: Bridging the Real-to-Sim Domain Gap in Autonomous Driving with Retrieval-Augmented Learning](https://mlsanity.com/paper/2501.12296/) (2025)
- [LiBrA-Net: Lie-Algebraic Bilateral Affine Fields for Real-Time 4K Video Dehazing](https://mlsanity.com/paper/2605.11508/) (2026)
- [RGBX-DiffusionDet: A Framework for Multi-Modal RGB-X Object Detection Using DiffusionDet](https://mlsanity.com/paper/2505.02586/) (2025)
- [AIR-VIEW: The Aviation Image Repository for Visibility Estimation of Weather, A Dataset and Benchmark](https://mlsanity.com/paper/2506.20939/) (2025)
- [UniDA3D: A Unified Domain-Adaptive Framework for Multi-View 3D Object Detection](https://mlsanity.com/paper/2603.27995/) (2026)
- [M3D-Stereo: A Multiple-Medium and Multiple-Degradation Dataset for Stereo Image Restoration](https://mlsanity.com/paper/2604.12917/) (2026)
- [FogDrive: A Multi-Modal Synthetic Driving Dataset for Perception under Graded Fog](https://mlsanity.com/paper/2607.22698/) (2026)
- [Enhancing Visual Perception in Foggy Conditions via Multiclass Fog Density Modeling](https://mlsanity.com/paper/2608.01572/) (2026)

## Abstract (arXiv)

To advance research in learning-based defogging algorithms, various synthetic fog datasets have been developed. However, existing datasets created using the Atmospheric Scattering Model (ASM) or real-time rendering engines often struggle to produce photo-realistic foggy images that accurately mimic the actual imaging process. This limitation hinders the effective generalization of models from synthetic to real data. In this paper, we introduce an end-to-end simulation pipeline designed to generate photo-realistic foggy images. This pipeline comprehensively considers the entire physically-based foggy scene imaging process, closely aligning with real-world image capture methods. Based on this pipeline, we present a new synthetic fog dataset named SynFog, which features both sky light and active lighting conditions, as well as three levels of fog density. Experimental results demonstrate that models trained on SynFog exhibit superior performance in visual perception and detection accuracy compared to others when applied to real-world foggy images.