The new tool allows creators, developers, and researchers to interact with extremely large and complex datasets in real-time.
According to the team, NeuralVDB adds machine learning to the library, thus introducing compact neural representations that dramatically reduce its memory footprint by up to 100x compared to NanoVDB, a GPU-accelerated version of OpenVDB introduced by NVIDIA last year.
Thanks to AI, 3D data can now be represented at an even higher resolution and at a much larger scale, allowing users to easily handle massive volumetric datasets.
On top of that, NeuralVDB also allows the weights of a frame to be used for the subsequent one and enables users to achieve temporal coherency by using the network results from the previous frame, bringing new possibilities for scientific and industrial use cases.
"Hitting this trifecta of dramatically reducing memory requirements, accelerating training, and enabling temporal coherency allows NeuralVDB to unlock new possibilities for scientific and industrial use cases, including massive, complex volume datasets for AI-enabled medical imaging, large-scale digital twin simulations, and more," commented NVIDIA.