We seek to sense the three dimensional (3D) volumetric distribution of scatterers in a heterogenous medium. An important case study for such a medium is the atmosphere. Atmospheric contents and their role in Earth’s radiation balance have significant uncertainties with regards to scattering components: aerosols and clouds. Clouds, made of water droplets, also lead to local effects as precipitation and shadows. Our sensing approach is computational tomography using passive multiangular imagery. For light-matter interaction that accounts for multiple-scattering, we use the 3D radiative transfer equation as a forward model. Volumetric recovery by inverting this model suffers from a computational bottleneck on large scales, which include many unknowns. Steps taken make this tomography tractable, without approximating the scattering order or angle range.