Deep Compositional Denoising for High‐quality Monte Carlo Rendering
Xianyao Zhang, Marco Manzi, Thijs Vogels, Henrik Dahlberg, Markus Groß, Marios Papas
Abstract
Abstract We propose a deep‐learning method for automatically decomposing noisy Monte Carlo renderings into components that kernel‐predicting denoisers can denoise more effectively. In our model, a neural decomposition module learns to predict noisy components and corresponding feature maps, which are consecutively reconstructed by a denoising module. The components are predicted based on statistics aggregated at the pixel level by the renderer. Denoising these components individually allows the use of per‐component kernels that adapt to each component's noisy signal characteristics. Experimentally, we show that the proposed decomposition module consistently improves the denoising quality of current state‐of‐the‐art kernel‐predicting denoisers on large‐scale academic and production datasets.