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.