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We derive computed tomography (CT) of a time-varying volumetric translucentobject, using a small number of moving cameras. We particularly focus onpassive scattering tomography, which is a non-linear problem. We demonstratethe approach on dynamic clouds, as clouds have a major effect on Earth'sclimate. State of the art scattering CT assumes a static object. Existing 4D CTmethods rely on a linear image formation model and often on significant priors.In this paper, the angular and temporal sampling rates needed for a properrecovery are discussed. If these rates are used, the paper leads to arepresentation of the time-varying object, which simplifies 4D CT tomography.The task is achieved using gradient-based optimization. We demonstrate this inphysics-based simulations and in an experiment that had yielded real-worlddata.

Spatiotemporal tomography based on scattered multiangular signals and its application for resolving evolving clouds using moving platforms