Neural Moment Transparency
| dc.contributor.author | Tsopouridis, Grigoris | en_US |
| dc.contributor.author | Vasilakis, Andreas Alexandros | en_US |
| dc.contributor.author | Fudos, Ioannis | en_US |
| dc.contributor.editor | Hu, Ruizhen | en_US |
| dc.contributor.editor | Charalambous, Panayiotis | en_US |
| dc.date.accessioned | 2024-04-30T08:19:17Z | |
| dc.date.available | 2024-04-30T08:19:17Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | We have developed a machine learning approach to efficiently compute per-fragment transmittance, using transmittance composed and accumulated with moment statistics, on a fragment shader. Our approach excels in achieving superior visual accuracy for computing order-independent transparency (OIT) in scenes with high depth complexity when compared to prior art. | en_US |
| dc.description.sectionheaders | Rendering and Optimization | |
| dc.description.seriesinformation | Eurographics 2024 - Short Papers | |
| dc.identifier.doi | 10.2312/egs.20241029 | |
| dc.identifier.isbn | 978-3-03868-237-0 | |
| dc.identifier.issn | 1017-4656 | |
| dc.identifier.pages | 4 pages | |
| dc.identifier.uri | https://doi.org/10.2312/egs.20241029 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/egs20241029 | |
| 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 → Neural networks; Rasterization; Visibility | |
| dc.subject | Computing methodologies → Neural networks | |
| dc.subject | Rasterization | |
| dc.subject | Visibility | |
| dc.title | Neural Moment Transparency | en_US |