Accelerated Volume Rendering with Volume Guided Neural Denoising
| dc.contributor.author | Jabbireddy, Susmija | en_US |
| dc.contributor.author | Li, Shuo | en_US |
| dc.contributor.author | Meng, Xiaoxu | en_US |
| dc.contributor.author | Terrill, Judith E. | en_US |
| dc.contributor.author | Varshney, Amitabh | en_US |
| dc.contributor.editor | Hoellt, Thomas | en_US |
| dc.contributor.editor | Aigner, Wolfgang | en_US |
| dc.contributor.editor | Wang, Bei | en_US |
| dc.date.accessioned | 2023-06-10T06:34:35Z | |
| dc.date.available | 2023-06-10T06:34:35Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Monte Carlo path tracing techniques create stunning visualizations of volumetric data. However, a large number of computationally expensive light paths are required for each sample to produce a smooth and noise-free image, trading performance for quality. High-quality interactive volume rendering is valuable in various fields, especially education, communication, and clinical diagnosis. To accelerate the rendering process, we combine learning-based denoising techniques with direct volumetric rendering. Our approach uses additional volumetric features that improve the performance of the denoiser in the post-processing stage. We show that our method significantly improves the quality of Monte Carlo volume-rendered images for various datasets through qualitative and quantitative evaluation. Our results show that we can achieve volume rendering quality comparable to the state-of-the-art at a significantly faster rate using only one sample path per pixel. | en_US |
| dc.description.sectionheaders | 3D | |
| dc.description.seriesinformation | EuroVis 2023 - Short Papers | |
| dc.identifier.doi | 10.2312/evs.20231042 | |
| dc.identifier.isbn | 978-3-03868-219-6 | |
| dc.identifier.pages | 49-53 | |
| dc.identifier.pages | 5 pages | |
| dc.identifier.uri | https://doi.org/10.2312/evs.20231042 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/evs20231042 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Computing methodologies -> Ray tracing; Neural networks | |
| dc.subject | Computing methodologies | |
| dc.subject | Ray tracing | |
| dc.subject | Neural networks | |
| dc.title | Accelerated Volume Rendering with Volume Guided Neural Denoising | en_US |