My poster on denoising and guided upsampling of Monte Carlo renderings has been accepted to SIGGRAPH 2022!
Here is the ACM Digital Library link of our poster, where the 30-second fast forward video in which I talk about the study is also available:
The author’s copy can be accessed here.
Here is the abstract of our study:
“Monte Carlo path tracing generates renderings by estimating the rendering equation using the Monte Carlo method. Studies focus on rendering a noisy image at the original resolution with a low sample per pixel count to decrease the rendering time. Image-space denoising is then applied to produce a visually appealing output. However, denoising process cannot handle the high variance of the noisy image accurately if the sample count is reduced harshly to finish the rendering in a shorter time. We propose a framework that renders the image at a reduced resolution to cast more samples than the harshly lowered sample count in the same time budget. The image is then robustly denoised, and the denoised result is upsampled using original resolution G-buffer of the scene as guidance.”
Hope to see you with new academic study blog posts in the future!