SeaThru-NeRF: Neural Radiance Fields in Scattering Media

Deborah Levy, Amit Peleg, Naama Pearl, Dan Rosenbaum, Derya Akkaynak, Simon Korman, Tali Treibitz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Research on neural radiance fields (NeRFs) for novel view generation is exploding with new models and extensions. However, a question that remains unanswered is what happens in underwater or foggy scenes where the medium strongly influences the appearance of objects. Thus far, NeRF and its variants have ignored these cases. However, since the NeRF framework is based on volumetric rendering, it has inherent capability to account for the medium's effects, once modeled appropriately. We develop a new rendering model for NeRFs in scattering media, which is based on the SeaThru image formation model, and suggest a suitable architecture for learning both scene information and medium parameters. We demonstrate the strength of our method using simulated and real-world scenes, correctly rendering novel photorealistic views underwater. Even more excitingly, we can render clear views of these scenes, removing the medium between the camera and the scene and reconstructing the appearance and depth of far objects, which are severely occluded by the medium. Our code and unique datasets are available on the project's website.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023
PublisherIEEE Computer Society
Number of pages10
ISBN (Electronic)9798350301298
StatePublished - 2023
Event2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 - Vancouver, Canada
Duration: 18 Jun 202322 Jun 2023

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919


Conference2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023

Bibliographical note

Funding Information:
Acknowledgements. The research was funded by Israel Science Foundation grant #680/18, Israeli Ministry of Science and Technology, European Union’s Horizon 2020 research and innovation programme GA 101094924 (ANERIS), the Leona M. and Harry B. Helmsley Charitable Trust, the Maurice Hatter Foundation, and Schmidt Marine Technology Partners. We thank Matan Yuval for substantial data contribution, Opher Bar-Nathan and Yuval Gold-fracht for help with experiments.

Publisher Copyright:
© 2023 IEEE.


  • Image and video synthesis and generation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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