Journal Article10.1007/S00371-012-0709-9
KD-tree based parallel adaptive rendering
TL;DR: A two-level framework for adaptive sampling in parallel is introduced to reduce the computation time and control the memory cost and novel kd-tree based strategies are introduced to measure space’s error value and generate anisotropic Poisson disk samples.
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Abstract: Multidimensional adaptive sampling technique is crucial for generating high quality images with effects such as motion blur, depth-of-field and soft shadows, but it costs a lot of memory and computation time We propose a novel kd-tree based parallel adaptive rendering approach First, a two-level framework for adaptive sampling in parallel is introduced to reduce the computation time and control the memory cost: in the prepare stage, we coarsely sample the entire multidimensional space and use kd-tree structure to separate it into several multidimensional subspaces; in the main stage, each subspace is refined by a sub kd-tree and rendered in parallel Second, novel kd-tree based strategies are introduced to measure space’s error value and generate anisotropic Poisson disk samples The experimental results show that our algorithm produces better quality images than previous ones
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