NVIDIA developed an open-source deep learning framework for sparse, large-scale, high-performance spatial intelligence
It unites fundamental AI operators using VDB. Generative physical AI with spatial intelligence relies on fVDB
fVDB AI operators built NanoVDB, which accelerates OpenVDB GPU. The framework supports sparse convolution and real-time optimised ray tracing
It works with the Kaolin Library for 3D deep learning and Warp for Pythonic spatial computation
In fVDB, differentiable operators like convolution, pooling, attention, and meshing are designed for high-performance 3D deep learning
NVIDIA NIM inference microservices let developers integrate fVDB core architecture with USD operations. NVIDIA Omniverse fVDB NIMs create OpenUSD geometry
With fVDB's premiere at SIGGRAPH, OpenVDB's ten-year evolution continues, advancing the ways in which real-world digital twins could assist various businesses
NVIDIA Research, NVIDIA DRIVE, and NVIDIA Omniverse projects use it to portray huge, complex real-world settings with great fidelity