Real-Time Neural Denoising for Volume Rendering Using Dual-Input Feature Fusion Network

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dc.contributor.authorXu, Chunxiaoen_US
dc.contributor.authorXu, Xinranen_US
dc.contributor.authorZhang, Jiatianen_US
dc.contributor.authorLiu, Yufeien_US
dc.contributor.authorCao, Yihengen_US
dc.contributor.authorZhao, Lingxiaoen_US
dc.contributor.editorWimmer, Michaelen_US
dc.contributor.editorAlliez, Pierreen_US
dc.contributor.editorWestermann, RĂĽdigeren_US
dc.date.accessioned2025-11-07T08:33:36Z
dc.date.available2025-11-07T08:33:36Z
dc.date.issued2025
dc.description.abstractDirect volume rendering (DVR) is a widely used technique in the visualization of volumetric data. As an important DVR technique, volumetric path tracing (VPT) simulates light transport to produce realistic rendering results, which provides enhanced perception and understanding for users, especially in the field of medical imaging. VPT, based on the Monte Carlo (MC) method, typically requires a large number of samples to generate noise-free results. However, in real-time applications, only a limited number of samples per pixel is allowed and significant noise can be created. This paper introduces a novel neural denoising approach that utilizes a new feature fusion method for VPT. Our method uses a feature decomposition technique that separates radiance into components according to noise levels. Our new decomposition technique mitigates biases found in contemporary decoupling denoising algorithms and shows better utilization of samples. A lightweight dual-input network is designed to correlate these components with noise-free ground truth. Additionally, for denoising sequences of video frames, we develop a learning-based temporal method that calculates temporal weight maps, blending reprojected results of previous frames with spatially denoised current frames. Comparative results demonstrate that our network performs faster inference than existing methods and can produce denoised output of higher quality in real time.en_US
dc.description.number6
dc.description.sectionheadersOriginal Article
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.70276
dc.identifier.issn1467-8659
dc.identifier.pages16 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70276
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70276
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectrendering
dc.subjectvolume rendering
dc.subjectvisualization
dc.subjectmedical imaging
dc.subjectscientific visualization
dc.subjectComputing methodologies→Rendering
dc.subjectRay tracing
dc.subjectHuman-centred computing→Visualization techniques
dc.titleReal-Time Neural Denoising for Volume Rendering Using Dual-Input Feature Fusion Networken_US
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