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

TL;DR

This paper introduces a real-time Monte Carlo denoising method that predicts kernel map encodings with a decoder, significantly reducing computation time while maintaining high denoising quality for very low samples per pixel.

Contribution

It proposes a novel kernel map encoding and decoding approach that halves denoising time and improves quality compared to existing kernel-prediction methods.

Findings

Halves denoising time for 1-spp images.

Achieves better denoising quality than neural bilateral grid methods.

Enables real-time denoising at low samples per pixel.

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.