NeuralVDB is a large-scale volume representation with AI-enabled data compression technology. It offers significant efficiency improvements over OpenVDB, the industry-standard library for simulating and rendering sparse volumetric data such as water, fire, smoke and clouds.
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Explore the benefits of NeuralVDB
Sparse volumetric data can be found across a larger body of work than ever before. Medical, industrial, robotics, graphics, and other fields demand high-resolution real-time simulations at unprecedented magnitudes but corresponding memory demands can conflict with hardware constraints. NerualVDB solves this issue by offering incredibly efficient memory representations at the cost of small to negligible quality loss, allowing for volumetric applications at extreme resolutions.
Built on OpenVDB
OpenVDB is the industry standard library for simulating and rendering dynamic volumes. Expanding on this foundation, NVIDIA introduced GPU-accelerated processing with NanoVDB for dramatically improved performance. Now, NeuralVDB takes all the features and optimizations of its predecessors and introduces a structure of compact neural representations that reduces memory footprints up to 100x. NeuralVDB is the next evolution of the VDB library.
VDB data is encoded into NeuralVDB representations through a compressor that prioritizes preserving information and temporal coherency between frames. NeuralVDB representations can then be readily decoded back into standard VDB volumes. In essence, NeuralVDB was built to be compatible with preexisting VDB pipelines. This ensures that adopting NeuralVDB into production workflows will be as seamless as possible.
Stay up to date on NVIDIA NeuralVDB news
Upping the Standard: NVIDIA Introduces NeuralVDB, Bringing AI and GPU Optimization to Award-Winning OpenVDB
Read the official announcement for NeuralVDB and how it revolutionizes volumetric data visualization across uses in special effects, medical research, and other industries.
NeuralVDB: High-resolution Sparse Volume Representation using Hierarchical Neural Networks
Explore the breakthrough research paper that introduces NeuralVDB, detailing how it applies machine learning innovations to VDB volume storage for maximized visual fidelity and scale.
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