  Monte Carlo rendering algorithms are widely used to produce photorealistic
computer graphics images. However, these algorithms need to sample a
substantial amount of rays per pixel to enable proper global illumination and
thus require an immense amount of computation. In this paper, we present a
hybrid rendering method to speed up Monte Carlo rendering algorithms. Our
method first generates two versions of a rendering: one at a low resolution
with a high sample rate (LRHS) and the other at a high resolution with a low
sample rate (HRLS). We then develop a deep convolutional neural network to fuse
these two renderings into a high-quality image as if it were rendered at a high
resolution with a high sample rate. Specifically, we formulate this fusion task
as a super resolution problem that generates a high resolution rendering from a
low resolution input (LRHS), assisted with the HRLS rendering. The HRLS
rendering provides critical high frequency details which are difficult to
recover from the LRHS for any super resolution methods. Our experiments show
that our hybrid rendering algorithm is significantly faster than the
state-of-the-art Monte Carlo denoising methods while rendering high-quality
images when tested on both our own BCR dataset and the Gharbi dataset.
\url{https://github.com/hqqxyy/msspl}
