Show simple item record

dc.contributor.authorHofmann, Nikolaien_US
dc.contributor.authorMartschinke, Janaen_US
dc.contributor.authorEngel, Klausen_US
dc.contributor.authorStamminger, Marcen_US
dc.contributor.editorYuksel, Cem and Membarth, Richard and Zordan, Victoren_US
dc.date.accessioned2020-10-30T18:18:25Z
dc.date.available2020-10-30T18:18:25Z
dc.date.issued2020
dc.identifier.issn2577-6193
dc.identifier.urihttps://doi.org/10.1145/3406181
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1145/3406181
dc.description.abstractIn this paper, we transfer machine learning techniques previously applied to denoising surface-only Monte Carlo renderings to path-traced visualizations of medical volumetric data. In the domain of medical imaging, path-traced videos turned out to be an efficient means to visualize and understand internal structures, in particular for less experienced viewers such as students or patients. However, the computational demands for the rendering of high-quality path-traced videos are very high due to the large number of samples necessary for each pixel. To accelerate the process, we present a learning-based technique for denoising path-traced videos of volumetric data by increasing the sample count per pixel; both through spatial (integrating neighboring samples) and temporal filtering (reusing samples over time). Our approach uses a set of additional features and a loss function both specifically designed for the volumetric case. Furthermore, we present a novel network architecture tailored for our purpose, and introduce reprojection of samples to improve temporal stability and reuse samples over frames. As a result, we achieve good image quality even from severely undersampled input images, as visible in the teaser image.en_US
dc.publisherACMen_US
dc.subjectComputing methodologies
dc.subjectRay tracing
dc.subjectNeural networks
dc.subjectImage processing
dc.titleNeural Denoising for Path Tracing of Medical Volumetric Dataen_US
dc.description.seriesinformationProceedings of the ACM on Computer Graphics and Interactive Techniques
dc.description.sectionheadersImage-Based Computing
dc.description.volume3
dc.description.number2
dc.identifier.doi10.1145/3406181


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record