Ashton E. W. Mason

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We show that the hierarchical level of detail optimization problem is equivalent to a constrained version of the Multiple Choice Knapsack Problem, and present a new algorithm whose solution to it is at least half as good as the optimal one. The advantage of the hierarchical algorithm is that it allows the use of hierarchical level of detail descriptions in(More)
We present a new method for representating the state spaces of hierarchical level of detail descriptions, or scene descriptions with multiple hierarchical levels of detail. This representation, called a level of detail graph, permits the investigation and exploration of the state spaces of non-hierarchical and hierarchical level of detail optimization(More)
We present a new hierarchical level of detail optimization algorithm that is predictive and so may be used for active frame rate control. We base our approach on earlier work demonstrating the equivalence of level of detail optimization to the Multiple Choice Knapsack Problem (MCKP). We show that this equivalence is broken for hierarchical level of detail(More)
The predictive hierarchical level of detail optimization algorithm of Mason and Blake is experimentally evaluated in the form of a practical application to hierarchical radiosity. In a novel approach the recursively subdivided patch hierarchy generated by a perceptually refined hierarchical radiosity algorithm is treated as a hierarchical level of detail(More)
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