Neural Partitioning Pyramids for Denoising Monte Carlo Renderings

Recent advancements in hardware-accelerated raytracing made it possible to achieve interactive framerates even for algorithms previously considered offline, such as path tracing. Interactive path tracing pipelines rely heavily on spatiotemporal denoising to produce a high-quality output from low-sample-count renderings. Such denoising is typically implemented as multiscale-kernel-based filters driven by lightweight U-Nets operating on pixels, and encoders operating on samples. In this work, we present a novel kernel architecture in the line of low-pass pyramid filters. Our architecture avoids the issues with the low-frequency response of previous such filters, resolving ringing, blotchiness, and box-shaped artefacts while improving overall detail. Instead of using classical downsampling and upsampling approaches, which are prone to aliasing, we let our weight predictor networks learn to partition the input radiance between pyramidal layers, predict kernels for denoising each partitioned and downscaled image, and then guide the upsampling process when combining layers. We present failure cases of pyramidal scale-composition in previous work and, through Fourier analysis, show how our method resolves them. Finally, we demonstrate state-of-the-art denoising performance.