Tsopouridis, GrigorisVasilakis, Andreas AlexandrosFudos, IoannisHu, RuizhenCharalambous, Panayiotis2024-04-302024-04-302024978-3-03868-237-01017-4656https://doi.org/10.2312/egs.20241029https://diglib.eg.org/handle/10.2312/egs20241029We 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.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Neural networks; Rasterization; VisibilityComputing methodologies → Neural networksRasterizationVisibility10.2312/egs.202410294 pages