Real‐time Monte Carlo Denoising with Weight Sharing Kernel Prediction Network

Abstract

Real‐time Monte Carlo denoising aims at removing severe noise under low samples per pixel (spp) in a strict time budget. Recently, kernel‐prediction methods use a neural network to predict each pixel's filtering kernel and have shown a great potential to remove Monte Carlo noise. However, the heavy computation overhead blocks these methods from real‐time applications. This paper expands the kernel‐prediction method and proposes a novel approach to denoise very low spp (e.g., 1‐spp) Monte Carlo path traced images at real‐time frame rates. Instead of using the neural network to directly predict the kernel map, i.e., the complete weights of each per‐pixel filtering kernel, we predict an encoding of the kernel map, followed by a high‐efficiency decoder with unfolding operations for a high‐quality reconstruction of the filtering kernels. The kernel map encoding yields a compact single‐channel representation of the kernel map, which can significantly reduce the kernel‐prediction network's throughput. In addition, we adopt a scalable kernel fusion module to improve denoising quality. The proposed approach preserves kernel prediction methods’ denoising quality while roughly halving its denoising time for 1‐spp noisy inputs. In addition, compared with the recent neural bilateral grid‐based real‐time denoiser, our approach benefits from the high parallelism of kernel‐based reconstruction and produces better denoising results at equal time.

Dynamic Diffuse Global Illumination Resampling

“…Recently, much work has been done to apply neural networks to denoising [FWHB21, HMS*20, MZV*20, MH20]. While our work specifically focuses on sample generation rather than denoising, many neural denoising approaches are directly compatible with our resampling method.…”

Morphological Anti-Aliasing Method for Boundary Slope Prediction

“…Aliasing is a common problem in both offline rendering [28,27,12,11,10,7,8,14,13,9] and real-time rendering [17,19,2,21,30,18]. Traditional anti-aliasing algorithms, supersampling anti-aliasing and multi-sampling anti-aliasing algorithms, have been the standard for antialiasing algorithms for more than ten years.…”

Extension: Adaptive Sampling with Implicit Radiance Field

“…By training a neural network denoiser through offline learning, it can filter noisy Monte Carlo rendering results into high-quality smooth output, greatly improving physics-based Availability of rendering techniques [13], common research includes predicting a filtering kernel based on g-buffer [2], using GAN to generate more realistic filtering results [28], and analyzing path space features Perform manifold contrastive learning to enhance the rendering effect of reflections [4], use weight sharing to quickly predict the rendering kernel to speed up reconstruction [6], filter and reconstruct high-dimensional incident radiation fields for unbiased reconstruction Drawing guide [12], etc. 2.…”