Robust deep residual denoising for monte carlo rendering

We propose a Deep Residual Learning based method that consistently outperforms both the state-of-the-art handcrafted denoisers and learning-based methods for single-image Monte Carlo denoising. Unlike the indirect nature of existing learning-based methods which estimate the parameters and kernel weights of a filter, we map directly the noisy input image to its noise-free counterpart. Our method uses only three common auxiliary features (depth, normal, and albedo), and this minimal requirement on auxiliary data simplifies both the training and integration of our method into most production rendering pipelines. We have evaluated our method on unseen images produced by a different renderer. Consistently high quality denoising results are obtained in all cases. We plan to release our training dataset as we are aware that the lack of publicly available training data is currently an entry barrier of learning based denoising research for Monte Carlo rendering.