By Jacob Munkberg1, Karthik Vaidyanathan1, Jon Hasselgren1, Petrik Clarberg1 and, Tomas Akenine-Möller1, 2
Intel Corporation1, Lund University2
Light field reconstruction algorithms can substantially decrease the noise in stochastically rendered images. Recent algorithms for defocus blur alone are both fast and accurate. However, motion blur is a considerably more complex type of camera effect, and as a consequence, current algorithms are either slow or too imprecise to use in high quality rendering. We extend previous work on real-time light field reconstruction for defocus blur to handle the case of simultaneous defocus and motion blur. By carefully introducing a few approximations, we derive a very efficient sheared reconstruction filter, which produces high quality images even for a low number of input samples. Our algorithm is temporally robust, and is about two orders of magnitude faster than previous work, making it suitable for both real-time rendering and as a post-processing pass for offline rendering.
Citation: Jacob Munkberg, Karthik Vaidyanathan, Jon Hasselgren, Petrik Clarberg and, Tomas Akenine-Möller: EGSR 2014
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