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

URI: https://research.nvidia.com/publication/2021-03_interactive-path-tracing-and-reconstruction-sparse-volumes

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.

Authors with CRIS profile

Nikolai Hofmann Chair of Visual Computing (LGDV)

Involved external institutions

Nvidia Corporation US 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.

BibTeX: Download