Predictive light transport models based on first-principles simulation approaches have been proposed for complex organic materials. The driving force behind these efforts has been the high-fidelity reproduction of material appearance attributes without one having to rely on the manipulation of ad hoc parameters. These models, however, are usually considered excessively time consuming for rendering and visualization applications requiring interactive rates. In this paper, we propose a strategy to address this open problem with respect to one of the most challenging of these organic materials, namely the human iris. More specifically, starting with the configuration of a predictive iridal light transport model on a parallel-computing platform, we analyze the sensitivity of iridal appearance attributes to key model running parameters in order to achieve an optimal balance between fidelity and performance. We believe that the proposed strategy represents a step toward the real-time and predictive synthesis of high-fidelity iridal images for rendering and visualization applications, and it can be extended to other organic materials.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.