Learning based compression for real-time rendering of surface light fields

ACM SIGGRAPH 2013 Posters - 2013
Download the publication : LearningBasedSLF.pdf [2.2Mo]   poster.pdf [11.6Mo]  
Photo-realistic rendering in real-time is a key challenge in computer graphics. A number of techniques where the light transport in a scene is pre-computed, compressed and used for real-time image synthesis have been proposed, e.g. [Ramamoorthi 2009]. We extend on this idea and present a technique where the radiance distribution in a scene, including arbitrarily complex materials and light sources, is pre-computed and stored as surface light fields (SLF) at each surface. An SLF describes the full appearance of each surface in a scene as a 4D function over the spatial and angular domains. An SLF is a complex data set with a large memory footprint often in the order of several GB per object in the scene. The key contribution in this work is a novel approach for compression of SLFs enabling real-time rendering of complex scenes. Our learning-based compression technique is based on exemplar orthogonal bases (EOB) [Gurumoorthy et al. 2010], and trains a compact dictionary of full-rank orthogonal basis pairs with sparse coefficients. Our results outperform the widely used CPCA method [Miandji et al. 2011] in terms of storage cost, visual quality and rendering speed. Compared to PRT techniques for real-time global illumination, our approach is limited to static scenes but can represent high frequency materials and any type of light source in a unified framework.

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BibTex references

  author       = "Miandji, Ehsan and Kronander, Joel and Unger, Jonas",
  title        = "Learning based compression for real-time rendering of surface light fields",
  booktitle    = "ACM SIGGRAPH 2013 Posters",
  year         = "2013"

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