ScatterNeRF: Seeing Through Fog with Physically-Based Inverse Neural Rendering

Andrea Ramazzina, Mario Bijelic, Stefanie Walz, Alessandro Sanvito, Dominik Scheuble, Felix Heide

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Vision in adverse weather conditions, whether it be snow, rain, or fog is challenging. In these scenarios, scattering and attenuation severly degrades image quality. Handling such inclement weather conditions, however, is essential to operate autonomous vehicles, drones and robotic applications where human performance is impeded the most. A large body of work explores removing weather-induced image degradations with dehazing methods. Most methods rely on single images as input and struggle to generalize from synthetic fully-supervised training approaches or to generate high fidelity results from unpaired real-world datasets. With data as bottleneck and most of today's training data relying on good weather conditions with inclement weather as outlier, we rely on an inverse rendering approach to reconstruct the scene content. We introduce ScatterNeRF, a neural rendering method which adequately renders foggy scenes and decomposes the fog-free background from the participating media - exploiting the multiple views from a short automotive sequence without the need for a large training data corpus. Instead, the rendering approach is optimized on the multi-view scene itself, which can be typically captured by an autonomous vehicle, robot or drone during operation. Specifically, we propose a disentangled representation for the scattering volume and the scene objects, and learn the scene reconstruction with physics-inspired losses. We validate our method by capturing multi-view In-the-Wild data and controlled captures in a large-scale fog chamber. Our code and datasets are available at https://light.princeton.edu/scatternerf.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages17911-17922
Number of pages12
ISBN (Electronic)9798350307184
DOIs
StatePublished - 2023
Event2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 - Paris, France
Duration: Oct 2 2023Oct 6 2023

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499

Conference

Conference2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
Country/TerritoryFrance
CityParis
Period10/2/2310/6/23

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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