Jabbireddy, SusmijaLi, ShuoMeng, XiaoxuTerrill, Judith E.Varshney, AmitabhHoellt, ThomasAigner, WolfgangWang, Bei2023-06-102023-06-102023978-3-03868-219-6https://doi.org/10.2312/evs.20231042https://diglib.eg.org:443/handle/10.2312/evs20231042Monte 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.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies -> Ray tracing; Neural networksComputing methodologiesRay tracingNeural networks10.2312/evs.2023104249-535 pages