3 results
Search Results
Now showing 1 - 3 of 3
Item Information Theory Tools for Scene Discretization(The Eurographics Association, 1999) Feixas, Miquel; Acebo, Esteve del; Bekaert, Philippe; Sbert, Mateu; Dani Lischinski and Greg Ward LarsonFinding an optimal discretization of a scene is an important but difficult problem in radiosity. The efficiency of hierarchical radiosity for instance, depends entirely on the subdivision criterion and strategy that is used. We study the problem of adaptive scene discretization from the point of view of information theory. In previous work, we have introduced the concept of mutual information, which represents the information transfer or correlation in a scene, as a complexity measure and presented some intuitive arguments and preliminary results concerning the relation between mutual information and scene discretization. In this paper, we present a more general treatment supporting and extending our previous findings to the level that the development of practical information theory-based tools for optimal scene discretization becomes feasible.Item Gathering for Free in RandomWalk Radiosity(The Eurographics Association, 1999) Sbert, Mateu; Brusi, Alex; Bekaert, Philippe; Dani Lischinski and Greg Ward LarsonWe present a simple technique that improves the efficiency of random walk algorithms for radiosity. Each generated random walk is used to simultaneously sample two distinct radiosity estimators. The first estimator is the commonly used shooting estimator, in which the radiosity due to self-emitted light at the origin of the random walk is recorded at each subsequently visited patch. With the second estimator, the radiosity due to self-emitted light at subsequent destinations is recorded at each visited patch. Closed formulae for the variance of the involved estimators allow to derive a cheap heuristic for combining the resulting radiosity estimates. Empirical results agree well with the heuristic prediction. A fair error reduction is obtained at a negligible additional cost.Item An Information Theory Framework for the Analysis of Scene Complexity(Blackwell Publishers Ltd and the Eurographics Association, 1999) Feixas, Miquel; Del Acebo, Esteve; Bekaert, Philippe; Sbert, MateuIn this paper we present a new framework for the analysis of scene visibility and radiosity complexity. We introduce a number of complexity measures from information theory quantifying how difficult it is to compute with accuracy the visibility and radiosity in a scene. We define the continuous mutual information as a complexity measure of a scene, independent of whatever discretisation, and discrete mutual information as the complexity of a discretised scene. Mutual information can be understood as the degree of correlation or dependence between all the points or patches of a scene. Thus, low complexity corresponds to low correlation and vice versa. Experiments illustrating that the best mesh of a given scene among a number of alternatives corresponds to the one with the highest discrete mutual information, indicate the feasibility of the approach. Unlike continuous mutual information, which is very cheap to compute, the computation of discrete mutual information can however be quite demanding. We will develop cheap complexity measure estimates and derive practical algorithms from this framework in future work.