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KD-tree based parallel adaptive rendering

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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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Authors and Affiliations

  1. National Key Laboratory of Integrated Information System Technology, Institute of Software, Chinese Academy of Sciences, Beijing, China

    Xiao-Dan Liu, Jia-Ze Wu & Chang-Wen Zheng

  2. Graduate University of Chinese Academy of Sciences, Beijing, China

    Xiao-Dan Liu

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  1. Xiao-Dan Liu
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  2. Jia-Ze Wu
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  3. Chang-Wen Zheng
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Correspondence to Xiao-Dan Liu.

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Liu, XD., Wu, JZ. & Zheng, CW. KD-tree based parallel adaptive rendering. Vis Comput 28, 613–623 (2012). https://doi.org/10.1007/s00371-012-0709-9

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  • DOI: https://doi.org/10.1007/s00371-012-0709-9

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