research-article

Interactive Path Tracing and Reconstruction of Sparse Volumes

Authors:

Nikolai Hofmann,

Jon Hasselgren,

Petrik Clarberg,

Jacob MunkbergAuthors Info & Claims

Proceedings of the ACM on Computer Graphics and Interactive Techniques, Volume 4, Issue 1

Article No.: 5, Pages 1 - 19

https://doi.org/10.1145/3451256

Published: 28 April 2021 Publication History

Abstract

We combine state-of-the-art techniques into a system for high-quality, interactive rendering of participating media. We leverage unbiased volume path tracing with multiple scattering, temporally stable neural denoising and NanoVDB [Museth 2021], a fast, sparse voxel tree data structure for the GPU, to explore what performance and image quality can be obtained for rendering volumetric data. Additionally, we integrate neural adaptive sampling to significantly improve image quality at a fixed sample budget. Our system runs at interactive rates at 1920 × 1080 on a single GPU and produces high quality results for complex dynamic volumes.

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Cited By

Pharr MWronski BSalvi MFajardo M(2024)Filtering After Shading With Stochastic Texture FilteringProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36512937:1(1-20)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3651293
Vaidyanathan KSalvi MWronski BAkenine-Moller TEbelin PLefohn A(2023)Random-Access Neural Compression of Material TexturesACM Transactions on Graphics10.1145/359240742:4(1-25)Online publication date: 26-Jul-2023
https://dl.acm.org/doi/10.1145/3592407
Hofmann NHasselgren JMunkberg J(2023)Joint Neural Denoising of Surfaces and VolumesProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/35854976:1(1-16)Online publication date: 16-May-2023
https://dl.acm.org/doi/10.1145/3585497
Show More Cited By

Index Terms

Interactive Path Tracing and Reconstruction of Sparse Volumes
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing
    2. Rendering
      1. Ray tracing
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image Proceedings of the ACM on Computer Graphics and Interactive Techniques

Proceedings of the ACM on Computer Graphics and Interactive Techniques Volume 4, Issue 1

April 2021

274 pages

EISSN:2577-6193

DOI:10.1145/3463840

Issue’s Table of Contents

Copyright © 2021 ACM.

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Publication History

Published: 28 April 2021

Published in PACMCGIT Volume 4, Issue 1

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Cited By

Pharr MWronski BSalvi MFajardo M(2024)Filtering After Shading With Stochastic Texture FilteringProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36512937:1(1-20)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3651293
Vaidyanathan KSalvi MWronski BAkenine-Moller TEbelin PLefohn A(2023)Random-Access Neural Compression of Material TexturesACM Transactions on Graphics10.1145/359240742:4(1-25)Online publication date: 26-Jul-2023
https://dl.acm.org/doi/10.1145/3592407
Hofmann NHasselgren JMunkberg J(2023)Joint Neural Denoising of Surfaces and VolumesProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/35854976:1(1-16)Online publication date: 16-May-2023
https://dl.acm.org/doi/10.1145/3585497
Lin DWyman CYuksel C(2021)Fast volume rendering with spatiotemporal reservoir resamplingACM Transactions on Graphics10.1145/3478513.348049940:6(1-18)Online publication date: 10-Dec-2021
https://dl.acm.org/doi/10.1145/3478513.3480499

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