Compressive Rendering of Multidimensional Scenes

  • Pradeep Sen
  • Soheil Darabi
  • Lei Xiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7082)

Abstract

Recently, we proposed the idea of using compressed sensing to reconstruct the 2D images produced by a rendering system, a process we called compressive rendering. In this work, we present the natural extension of this idea to multidimensional scene signals as evaluated by a Monte Carlo rendering system. Basically, we think of a distributed ray tracing system as taking point samples of a multidimensional scene function that is sparse in a transform domain. We measure a relatively small set of point samples and then use compressed sensing algorithms to reconstruct the original multidimensional signal by looking for sparsity in a transform domain. Once we reconstruct an approximation to the original scene signal, we can integrate it down to a final 2D image which is output by the rendering system. This general form of compressive rendering allows us to produce effects such as depth-of-field, motion blur, and area light sources, and also renders animated sequences efficiently.

Keywords

Mean Square Error Computer Graphic Fourier Domain Motion Blur Compress Sense Reconstruction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pradeep Sen
    • 1
  • Soheil Darabi
    • 1
  • Lei Xiao
    • 1
  1. 1.Advanced Graphics LabUniversity of New MexicoAlbuquerqueUSA

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