Interactive neural cascade denoising for 1-spp Monte Carlo images

Monte Carlo (MC) path tracing is known for its high fidelity and heavy computational cost.With the development of neural networks, the kernel-based post-processing method has succeeded in denoising noisy images under low sampling rates, but the complex network structure impedes its deployment in interactive applications. In this paper, we propose a lightweight cascaded network which progressively denoises 1-spp Monte Carlo images through both pixel and kernel prediction methods. A primary denoised image is generated by the pixel prediction network at the first stage, which is then fed to the kernel prediction network to obtain multi-resolution kernels. In addition, to take full advantage of the auxiliary buffers, we introduce a bilateral method during image reconstruction. Experimental results show that our approach achieves state-of-the-art denoising qualities for 1-spp images at an interactive frame speed.

Automated summary

In this paper, a lightweight cascaded network is proposed to denoise 1-spp Monte Carlo images through pixel and kernel prediction methods. Experimental results show that the approach achieves state-of-the-art denoising qualities for 1-spp images at an interactive frame speed.