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Now showing 1 - 6 of 6
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    Information Theory Tools for Scene Discretization
    (The Eurographics Association, 1999) Feixas, Miquel; Acebo, Esteve del; Bekaert, Philippe; Sbert, Mateu; Dani Lischinski and Greg Ward Larson
    Finding 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.
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    Gathering for Free in RandomWalk Radiosity
    (The Eurographics Association, 1999) Sbert, Mateu; Brusi, Alex; Bekaert, Philippe; Dani Lischinski and Greg Ward Larson
    We 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.
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    A Pyramidal Hemisphere Subdivision Method for Monte Carlo Radiosity
    (Eurographics Association, 1999) Jolivet, Vincent; Plemenos, Dimitri; Sbert, Mateu
    In this paper we present a new method to improve Monte Carlo radiosity by sending more rays towards selected directions. More precisely, we determine regions of the scene where the distribution of the power must be done more accurately. The number of rays sent in a direction is a function of the number of patches contained in a region, a region being a pyramid defined by the centre of the shooting patch and a spherical triangle on the surface of a hemisphere surrounding the patch. Thus, the rays shot from a patch do not have all the same power. The new method allows us not only to obtain fine details much sooner and with lower cost, but also the overall efficiency is considerably increased.
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    An Integral Geometry Based Method for Fast Form-Factor Computation
    (Blackwell Science Ltd and the Eurographics Association, 1993) Sbert, Mateu
    Monte Carlo techniques have been widely used in rendering algorithms for local integration. For example, to compute the contribution of a patch to the luminance of another. In the present paper we propose an algorithm based on Integral geometry where Monte Carlo is applied globally. We give some results of the implementation to validate the proposition and we study the error of the technique, as well as its complexity.
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    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, Mateu
    In 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.
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    Optimal Source Selection in Shooting Random Walk Monte Carlo Radiosity
    (Blackwell Publishers Ltd and the Eurographics Association, 1997) Sbert, Mateu
    In this paper we study how to optimally select between different sources in shooting random walk Monte Carlo Radiosity. Until now the probability of selecting a source has been made proportional to the importance of that source for the region of interest. We will show here that, whenever the transition probabilities are the Form Factors, this is not optimal, and will consequently give the optimal case. This will correspond to probabilities proportional to the square root of importances, rather than to importances themselves.