Ken Museth
ABSTRACT
We introduce a sparse volumetric data structure, dubbed NanoVDB, which is portable to both C++11 and C99 as well as most graphics APIs, e.g. CUDA, OpenCL, OpenGL, WebGL, DirectX 12, OptiX, HLSL, and GLSL. As indicated by its name, NanoVDB is a mini-version of the much bigger OpenVDB library, both in terms of functionality and scope. However, NanoVDB offers one major advantage over OpenVDB, namely support for GPUs. As such it is applicable to both CPU and GPU accelerated simulation and rendering of high-resolution sparse volumes. In fact, it has already been adopted for real-time applications by several commercial renders and digital content creation tools, e.g. Autodesk’s Arnold, Blender, SideFX’s Houdini, and NVIDIA’s Omniverse just to mention a few.
Supplemental Material
- Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, and Frédo Durand. 2019. Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures. 38, 6 (2019).Google Scholar
Digital Library
- Ken Museth. 2013. VDB: High-resolution Sparse Volumes with Dynamic Topology. ACM Trans. Graph. 32, 3, Article 27 (July 2013), 22 pages. https://doi.org/10.1145/2487228.2487235Google ScholarDigital Library
- Ken Museth. 2014. Hierarchical Digital Differential Analyzer for Efficient Ray-Marching in OpenVDB. Association for Computing Machinery.Google Scholar
- Michael B. Nielsen and Ken Museth. 2006. Dynamic Tubular Grid: An Efficient Data Structure and Algorithms for High Resolution Level Sets. 26, 3 (2006).Google Scholar
- Rajsekhar Setaluri, Mridul Aanjaneya, Sean Bauer, and Eftychios Sifakis. 2014. SPGrid: A Sparse Paged Grid Structure Applied to Adaptive Smoke Simulation. 33, 6 (2014).Google ScholarDigital Library
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