A Local Image Reconstruction Algorithm for Stochastic Rendering

Stochastic renderers produce unbiased but noisy images of scenes that include the advanced camera effects of motion and defocus blur and possibly other effects such as transparency. We present a simple algorithm that selectively adds bias in the form of image space blur to pixels that are unlikely to have high frequency content in the final image. For each pixel, we sweep once through a fixed neighborhood of samples in front to back order, using a simple accumulation scheme. We achieve good quality images with only 16 samples per pixel, making the algorithm potentially practical for interactive stochastic rendering in the near future.


Peter Shirley (NVIDIA)
Jonathan Cohen (NVIDIA)
Eric Enderton (NVIDIA)
Morgan McGuire (Williams College)

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