Layered Reconstruction for Defocus and Motion Blur

Jacob Munkberg
Intel Corporation

Karthik Vaidyanathan
Intel Corporation

Jon Hasselgren
Intel Corporation

Petrik Clarberg
Intel Corporation

Tomas Akenine-Möller
Lund University & Intel Corporation

Eurographics Symposium of Rendering, 2014.


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.


Since we published this paper, Jon and Jacob have optimized this algorithm a lot, and published an article called "Practical Layered Reconstruction for Defocus and Motion Blur". The optimized code can be found here.


An author-generated version of the paper. [pdf 15.5 MB]
Source code

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