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Adaptive cluster rendering via regression analysis

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Abstract

Monte Carlo ray tracing suffers noise and aliasing because of low sampling rate. We show that sparse samples can be used to generate high quality images based on feature cluster and regression analysis. Our algorithm has two main stages: adaptive sampling and polynomial reconstruction. In sampling stage, rendering space are organized into clusters based on their features. A feature vector is used to distinguish the different features, which contains gradient, variance and position. Clusters are progressively modified by adaptive sampling. In reconstruction stage, we model each cluster by smooth polynomial functions using regression analysis. The final image is synthesized by integrating these functions. The experiments show that our algorithm generates higher quality images than the previous methods.

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Authors and Affiliations

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

    Xiao Dan Liu

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

    Xiao Dan Liu

  3. Integrated Information System Technology Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, China

    Chang Wen Zheng

Authors
  1. Xiao Dan Liu
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  2. Chang Wen Zheng
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Correspondence to Xiao Dan Liu.

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Liu, X.D., Zheng, C.W. Adaptive cluster rendering via regression analysis. Vis Comput 31, 105–114 (2015). https://doi.org/10.1007/s00371-013-0914-1

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  • DOI: https://doi.org/10.1007/s00371-013-0914-1

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