Compressive Rendering of Multidimensional Scenes

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.