2012
DOI: 10.1007/s00371-012-0709-9
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KD-tree based parallel adaptive rendering

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 structu… Show more

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Cited by 9 publications
(3 citation statements)
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References 26 publications
(40 reference statements)
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“…To progressively render virtual scenes from a blurred state to a clear state, sequences of LOD models are transferred from coarse to fine. Reference [ 10 ] proposed a novel kd-tree based on a parallel adaptive rendering approach. A two-level framework for adaptive sampling in parallel is introduced to reduce the computation time and to lower the memory usage.…”
Section: Related Workmentioning
confidence: 99%
“…To progressively render virtual scenes from a blurred state to a clear state, sequences of LOD models are transferred from coarse to fine. Reference [ 10 ] proposed a novel kd-tree based on a parallel adaptive rendering approach. A two-level framework for adaptive sampling in parallel is introduced to reduce the computation time and to lower the memory usage.…”
Section: Related Workmentioning
confidence: 99%
“…Clargerg et al analyze the environment light by wavelet and focus on the high weight area of the scene [8]. To render more effects, adaptive sampling is used in multidimensional sampling [1,9]. To avoid dimensionality curse, some method can get non-image variance based on image analysis [10,11].…”
Section: Previous Workmentioning
confidence: 99%
“…Eitz et al [6] calculate the similarity with the help of "visual words", which are local shapes obtained from 2D views. Liu et al [7] developed a search algorithm based on both geometry and color.…”
Section: Introductionmentioning
confidence: 99%