We derive computed tomography (CT) of a time-varying volumetric translucent object, using a small number of moving cameras. We particularly focus on passive scattering tomography, which is a non-linear problem. We demonstrate the approach on dynamic clouds, as clouds have a major effect on 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. If these rates are used, the paper leads to a representation of the time-varying object, which simplifies 4D CT tomography. The task is achieved using gradient-based optimization. We demonstrate this in physics-based simulations and in an experiment that had yielded real-world data.
@article{arxiv.2012.03223,
title = {Spatiotemporal tomography based on scattered multiangular signals and its application for resolving evolving clouds using moving platforms},
author = {Roi Ronen and Yoav Y. Schechner and Eshkol Eytan},
journal= {arXiv preprint arXiv:2012.03223},
year = {2020}
}