PARS – Path Recycling and Sorting for Fast Inverse Rendering
We derive an efficient framework for inverse-rendering and specifically computed tomography (CT) of volumetric scattering objects. We focus on clouds, which have a key role in the climate system and require efficient analysis at a huge scale.
Prior art shows that scattering CT can rely on Monte-Carlo light transport. This approach usually iterates differentiable radiative transfer, requiring many sampled paths per iteration. We present an acceleration approach: Path recycling and sorting (PARS). It efficiently uses paths from previous iterations, for estimating a loss gradient at the current iteration. This reduces the iteration runtime. PARS enables further efficient realizations. Specifically, sorting paths according to their size accelerates implementations on a graphical process unit (GPU).
PARS, however, requires a correction operation for unbiased gradient estimation. We derive the theory of PARS and demonstrate its efficiency on cloud CT of both synthetic and real-world scenes. Moreover, we demonstrate PARS on simple reflectometry examples.
* M.Sc. student under the supervision of Professor Yoav Y. Schechner.