Interactive Reconstruction of Monte Carlo Image Sequences using a Recurrent Denoising Autoencoder
Owing to the immense popularity of ray-tracing and path tracing rendering algorithms for visual effects, there has been a surge of interest in developing filtering and reconstruction methods to deal with the noise present in these Monte Carlo renderings. Despite the focus on a large sampling rate (upto thousands of samples per pixel before filtering), even the fastest ray tracers are limited to a few rays per pixel, and a low sampling budget would be realistic for the foreseeable future. This paper proposes a learning-based approach for reconstruction of global illumination with very low sampling budgets (as low as 1 spp) at interactive rates. At 1 sample per pixel (spp), the Monte Carlo integration of indirect illumination results in very noisy images, and the problem can therefore be framed as reconstruction instead of denoising. Previous works on offline and interactive denoising for Monte Carlo rendering suffer from a trade-off between speed and performance, require user-defined parameters, and scale poorly to large scenes. Inspired by the progress in single image restoration (denoising) using deep learning, the authors propose a deep learning based approach which leverages an encoder-decoder architecture and recurrent connections for improved temporal consistency. The proposed model requires no user guidance, is end-to-end trainable and is able to exploit auxiliary pixel features for improved performance. ...