A Data Efficiency Study of Synthetic Fog for Object Detection Using the Clear2Fog Pipeline
Object detection in adverse weather is critical for the safety of autonomous vehicles; however, the scarcity of labelled, real-world foggy data remains a significant bottleneck. In this paper, we propose Clear2Fog (C2F), an end-to-end, physics-based pipeline that simulates fog for clear-weather datasets under a unified camera and LiDAR framework. C2F combines monocular depth estimation with a novel atmospheric light estimation method to improve the physical consistency of synthetic fog generation while reducing structural artefacts and chromatic biases observed in existing frameworks. Utilising a training set of 270,000 images from the Waymo Open Dataset, we conduct an extensive data effi...
| Authors | Mohamed Ahmed Mohamed, Xiaowei Huang |
|---|---|
| Primary category | cs.CV |
| Published | 12 May 2026 |
| Last updated at source | 13 August 2026 |
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