4D Cloud Scattering Tomography

Roi Ronen, Yoav Y. Schechner, Eshkol Eytan

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

Abstract

We derive computed tomography (CT) of a time-varying volumetric scattering object, using a small number of moving cameras. We focus on passive tomography of dynamic clouds, as clouds have a major effect on the Earth's climate. State of the art scattering CT assumes a static object. Existing 4D CT methods rely on a linear image formation model and often on significant priors. In this paper, the angular and temporal sampling rates needed for a proper recovery are discussed. Spatiotemporal CT is achieved using gradient-based optimization, which accounts for the correlation time of the dynamic object content. We demonstrate this in physics-based simulations and on experimental real-world data.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
Pages5500-5509
Number of pages10
ISBN (Electronic)9781665428125
DOIs
StatePublished - 2021
Event18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 - Virtual, Online, Canada
Duration: 11 Oct 202117 Oct 2021

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Conference

Conference18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
Country/TerritoryCanada
CityVirtual, Online
Period11/10/2117/10/21

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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