Interactive Path Tracing and Reconstruction of Sparse Volumes
Hofmann N, Hasselgren J, Clarberg P, Munkberg J (2021)
Publication Language: English
Publication Type: Conference contribution, Original article
Publication year: 2021
Conference Proceedings Title: Proceedings of the ACM on Computer Graphics and Interactive Techniques
Event location: Online
DOI: 10.1145/3451256
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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United States (USA) (US)How to cite
APA:
Hofmann, N., Hasselgren, J., Clarberg, P., & Munkberg, J. (2021). Interactive Path Tracing and Reconstruction of Sparse Volumes. In ACM (Eds.), Proceedings of the ACM on Computer Graphics and Interactive Techniques. Online.
MLA:
Hofmann, Nikolai, et al. "Interactive Path Tracing and Reconstruction of Sparse Volumes." Proceedings of the I3D 2021, Online Ed. ACM, 2021.
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