Importance-Driven Hierarchical Stochastic Ray Radiosity

In this paper we present a hierarchical Monte-Carlo radiosi ty algorithm driven by the view importance. The algorithm makes to possible to concentrate the computation l effort on solution in the immediate environment of the observer, trading the low solution quality in inv isible areas for better quality in areas that are visible for the observer. This is achieved by modifying the s ampling probabilities of scene elements so that more samples are concentrated in the area of high importance and by extending the subdivision oracle function so that the subdivision is coarser in areas of low import ance. This paper extends the previous work by introducing a combination of hierarchical refinement and vi ew mportance driven method for Monte-Carlo radiosity.

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