Abstract

Global illumination algorithms are regarded as computationally intensive. This cost is a practical problem when producing animations or when interactions with complex models are required. Several algorithms have been proposed to address this issue. Roughly, two families of methods can be distinguished. The first one aims at providing interactive feedback for lighting design applications. The second one gives higher priority to the quality of results, and therefore relies on offline computations. Recently, impressive advances have been made in both categories. In this report, we present a survey and classification of the most up-to-date of these methods.

This paper presents a new hierarchical simulation algorithm allowing the calculation of radiosity solutions for time-dependent scenes where all motion is known a priori. Such solutions could, for instance, be computed to simulate subtle lighting effects (indirect lighting) in animation systems, or to obtain highquality synthetic image sequences to blend with live action video and film. We base our approach on a Space-Time hierarchy, adding a life span to hierarchical surface elements, and present an integrated formulation of Hierarchical Radiosity with this extended hierarchy. We discuss the expected benefits of the technique, review the challenges posed by the approach, and propose first solutions for these issues, most notably for the space-time refinement strategy. We show that a short animation sequence can be computed rapidly at the price of a sizeable memory cost. These results confirm the potential of the approach while helping to identify areas of promising future work.

The calculation of radiant energy balance in complex scenes has been made possible by hierarchical radiosity methods based on clustering mechanisms. Although clustering offers an elegant theoretical solution by reducing the asymptotic complexity of the algorithm, its practical use raises many difficulties, and may result in image artifacts or unexpected behavior. This paper proposes a detailed analysis of the expectations placed on clustering and compares the relative merits of existing, as well as newly introduced, clustering algorithms. This comparison starts from the precise definition of various clustering strategies based on a taxonomy of data structures and construction algorithms, and proceeds to an experimental study of the clustering behavior for real‐world scenes. Interestingly, we observe that for some scenes light is difficult to simulate even with clustering. Our results lead to a series of observations characterizing the adequacy of clustering methods for meeting such diverse goals as progressive solution improvement, efficient ray casting acceleration, and faithful representation of object density for approximate visibility calculations.