1. [Publications](/publications) 2. Joint Neural Denoising of Surfaces and Volumes # Joint Neural Denoising of Surfaces and Volumes ![](/sites/default/files/styles/wide/public/publications/teaser_1.PNG?itok=L3hNFKfP) **Abstract** Denoisers designed for surface geometry rely on noise-free feature guides for high quality results. However, these guides are not readily available for volumes. Our method enables combined volume and surface denoising in real time from low sample count (4 spp) renderings. The rendered image is decomposed into volume and surface layers, leveraging spatio-temporal neural denoisers for both components. The individual signals are composited using learned weights and denoised transmittance. Our architecture outperforms current denoisers in scenes containing both surfaces and volumes, and produces temporally stable results at interactive rates. ## Authors Nikolai Hofmann (NVIDIA and Visual Computing, University of Erlangen-Nuremberg, Germany) [Jon Hasselgren](/person/jon-hasselgren) [Jacob Munkberg](/person/jacob-munkberg) ## Publication Date Friday, March 24, 2023 ## Published in [I3D 2023 ](https://i3dsymposium.org/2023/cfp.html) ## Research Area [Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence) [Computer Graphics](/research-area/computer-graphics) [Real-Time Rendering](/research-area/real-time-rendering) ## Uploaded Files [Preprint](https://d1qx31qr3h6wln.cloudfront.net/publications/hofmann_i3d2023.pdf "Open file in new window")27.24 MB [Video](https://d1qx31qr3h6wln.cloudfront.net/publications/jointdenoise_hq3.mp4 "Open video in new window")337.97 MB [Image viewer](https://d1qx31qr3h6wln.cloudfront.net/publications/viewer.zip "Open archive in new window")53.53 MB ## Copyright Copyright by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or . The definitive version of this paper can be found at ACM's Digital Library .