Phys.org March 28, 2023
One way to study clouds is to use spaceborne imagers, but these imagers still face challenges of efficiency and scalability. Researchers in Israel have developed an effective inverse rendering framework for recovering the 3D distribution of clouds. They focused on clouds which have a key role in the climate system and require efficient analysis at a huge scale. Data for such reconstruction are multiview images of each cloud taken simultaneously. This acquisition mode is expected by upcoming future spaceborne imagers, such as Cloud Computed Tomography (CT). Prior art showed that scattering CT can rely on Monte–Carlo light transport. This approach usually iterates differentiable radiative transfer, requiring many sampled paths per iteration. They presented an acceleration approach: path recycling and sorting (PARS) which efficiently used paths from previous iterations for estimating a loss gradient at the current iteration. This reduced the iteration run time, PARS enabled sorting paths according to their size accelerates implementations on a graphical processing unit. Monte–Carlo integration methods were used for PARS correction operations. They derived the theory of PARS and demonstrated its efficiency on cloud tomography of both synthetic and real-world scenes… read more. Open Access TECHNICAL ARTICLEÂ
Cloud tomography. A formation flight captures simultaneous multiview images of a cloud… Credit: INTELLIGENT COMPUTING, 3 Jan 2023, Vol 2, Article ID: 0007Â