Domain Transfer for Long Distance Imaging and Vision through Scattering Media

written by: Zheng Shi¹, Ethan Tseng¹, Mario Bijelic¹, Werner Ritter, Felix Heide

¹Zheng Shi, Ethan Tseng and Mario Bijelic have contributed equally to the work.

Adverse weather conditions, including snow, rain, and fog, pose a major challenge for both human and computer vision. Handling these environmental conditions is essential for safe decision making, especially in autonomous vehicles, robotics, and drones. Most of today’s supervised imaging and vision approaches, however, rely on training data collected in the real world that is biased towards good weather conditions, with dense fog, snow, and heavy rain as outliers in these datasets. Without training data, let alone paired data, existing autonomous vehicles often limit themselves to good conditions and stop when dense fog or snow is detected. In this work, we tackle the lack of supervised training data by combining synthetic and indirect supervision. We present ZeroScatter, a domain transfer method for converting RGB-only captures taken in adverse weather into clear daytime scenes. ZeroScatter exploits model-based, temporal, multi-view, multi-modal, and adversarial cues in a joint fashion, allowing us to train on unpaired, biased data. We assess the proposed method on in-the-wild captures, and the proposed method outperforms existing monocular descattering approaches by 2.8 dB PSNR on controlled fog chamber measurements.

You should definitely visit CVF and take a look on our CVPR publication:

[Proceedings of the IEEE/CVF]

The data is available here:

Further information can be found here:
[Project Page]

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