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.
翻译:蒙特卡洛(Monte Carlo)的实时拆解旨在在严格的预算时间内消除每像素低样本下每像素(Spp)的强烈噪音。最近,内核透析方法使用神经网络来直接预测每像素过滤内核,并显示出巨大的潜力去掉蒙特卡洛的噪音。然而,大量计算间接费用将这些方法从实时应用中阻断。本文扩展了内核透析方法,并提出了一种新颖方法,以极低的比重(例如,1-Spp)蒙特卡洛路径追踪到的图像。最近,内核透析方法使用神经网络直接预测每像素过滤内核的完整重量来预测每个像素过滤内核的内核内核内核内核,我们预测内核图的编码,随后是高效解密操作,以高质量的方式重建过滤内核内核。内核图的缩缩缩缩缩缩缩略图,这可以大大降低内核内核内核的内核图,比对内核内核内核的内核的内核图的比值,从而改进了内核对内核的内核的内核的内核的内核的内核的内核的内核质量。