  Auxiliary features such as geometric buffers (G-buffers) and path descriptors
(P-buffers) have been shown to significantly improve Monte Carlo (MC)
denoising. However, recent approaches implicitly learn to exploit auxiliary
features for denoising, which could lead to insufficient utilization of each
type of auxiliary features. To overcome such an issue, we propose a denoising
framework that relies on an explicit pixel-wise guidance for utilizing
auxiliary features. First, we train two denoisers, each trained by a different
auxiliary feature (i.e., G-buffers or P-buffers). Then we design our ensembling
network to obtain per-pixel ensembling weight maps, which represent pixel-wise
guidance for which auxiliary feature should be dominant at reconstructing each
individual pixel and use them to ensemble the two denoised results of our
denosiers. We also propagate our pixel-wise guidance to the denoisers by
jointly training the denoisers and the ensembling network, further guiding the
denoisers to focus on regions where G-buffers or P-buffers are relatively
important for denoising. Our result and show considerable improvement in
denoising performance compared to the baseline denoising model using both
G-buffers and P-buffers.
