Accelerated Volume Rendering with Volume Guided Neural Denoising
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description
CCS Concepts: Computing methodologies -> Ray tracing; Neural networks
@inproceedings{10.2312:evs.20231042,
booktitle = {EuroVis 2023 - Short Papers},
editor = {Hoellt, Thomas and Aigner, Wolfgang and Wang, Bei},
title = {{Accelerated Volume Rendering with Volume Guided Neural Denoising}},
author = {Jabbireddy, Susmija and Li, Shuo and Meng, Xiaoxu and Terrill, Judith E. and Varshney, Amitabh},
year = {2023},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-219-6},
DOI = {10.2312/evs.20231042}
}