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.
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Sen, P., Darabi, S., Xiao, L. (2011). Compressive Rendering of Multidimensional Scenes. In: Cremers, D., Magnor, M., Oswald, M.R., Zelnik-Manor, L. (eds) Video Processing and Computational Video. Lecture Notes in Computer Science, vol 7082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24870-2_7
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DOI: https://doi.org/10.1007/978-3-642-24870-2_7
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