Jacob Munkberg, Jon Hasselgren
We propose an efficient and robust denoiser for Monte-Carlo path tracing that exploits individual samples from the renderer instead of only pixel aggregates. Individual samples are partitioned into layers, which are filtered separately, giving the network more freedom to handle outliers and complex visibility. Finally the layers are alpha-composited. The entire system is trained end-to-end, with learned layer partitioning, filter kernels and compositing. We show results on global illumination with stochastic primary visibility, e.g., motion blur and defocus blur. We obtain similar quality as recent state-of-the-art sample-based denoisers at a fraction of the computational cost and memory requirements.
Lorenzo Tessari, Johannes Hanika, Carsten Dachsbacher, Marc Droske
Specular aliasing is a problem that can make seemingly simple scenes notoriously hard to render efficiently: small geometric features with high curvature and near specular reflectance properties result in tiny lighting features which are difficult to resolve at low sample counts per pixel. This is especially apparent in scenes including fluid simulation, which often feature fast moving elements such as spray particles. LEAN and LEADR mapping can be used to convert geometric surface detail to anisotropic surface roughness in a preprocess. For FX elements fine geometric detail can be represented as participating media. Both approaches are only valid in the far-field regime where the geometric detail is much smaller than a pixel and in the meso-scale the challenge of resolving highlights remains. Fast motion and the relatively long shutter intervals, commonly used in movie production, lead to strong variation of the surface normals seen under a pixel over time, thus aggravating the problem. Recently proposed specular anti aliasing approaches preintegrate geometric curvature under the pixel footprint for one specific ray to achieve noise free images at very low sample counts. To close the gap between LEADR mapping and precise ray tracing, we extend specular anti aliasing to anisotropic surface roughness and to account for the temporal surface normal variation due to motion blur.
Xiaoxu Meng, Quan Zheng, Amitabh Varshney, Gurprit Singh, Matthias Zwicker
Real-time denoising for Monte Carlo rendering remains a critical challenge with regard to the demanding requirements of both high fidelity and low computation time. In this paper, we propose a novel and practical deep learning approach to robustly denoise Monte Carlo images rendered at sampling rates as low as a single sample per pixel (1-spp). This causes severe noise, and previous techniques strongly compromise final quality to maintain real-time denoising speed. We develop an efficient convolutional neural network architecture to learn to denoise noisy inputs in a data-dependent, bilateral space. Our neural network learns to generate a guide image for first splatting noisy data into the grid, and then slicing it to read out the denoised data. To seamlessly integrate bilateral grids into our trainable denoising pipeline, we leverage a differentiable bilateral grid, called neural bilateral grid, which enables end-to-end training. In addition, we also show how we can further improve denoising quality using a hierarchy of multi-scale bilateral grids. Our experimental results demonstrate that this approach can robustly denoise 1-spp noisy input images at real-time frame rates (a few milliseconds per frame). At such low sampling rates, our approach outperforms state-of-the-art techniques based on kernel prediction networks both in terms of quality and speed, and it leads to significantly improved quality compared to the state-of-the-art feature regression technique.