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
} }
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