Ted Hisokawa
Jul 30, 2024 05:17
NVIDIA’s new fVDB framework leverages deep learning for large-scale 3D data, enhancing spatial intelligence in physical AI applications.
NVIDIA has unveiled its new deep-learning framework, fVDB, designed to build spatial intelligence from real-world 3D data. According to the NVIDIA Technical Blog, fVDB aims to solve the inefficiencies and performance bottlenecks that come with piecing together various libraries for spatial intelligence.
Challenges in Spatial Intelligence
Generative physical AI models require spatial intelligence to understand and navigate the 3D space of the physical world. Traditionally, developers have had to use a patchwork of different libraries to build frameworks for spatial intelligence, leading to bugs, inefficiencies, and performance bottlenecks.
Introducing fVDB
NVIDIA’s fVDB framework is designed to handle sparse, large-scale, and high-performance spatial intelligence. Leveraging OpenVDB, an industry-standard for the efficient storage and simulation of sparse volumetric data, fVDB integrates deep learning operators with NanoVDB, NVIDIA’s GPU-accelerated implementation of OpenVDB.
fVDB is an open-source extension to PyTorch, enabling a complete set of deep-learning operations on large 3D data. Key capabilities include compatibility with existing VDB datasets, a unified API for neural network training, ray tracing, and rendering, and faster, more scalable performance.
Applications of fVDB
fVDB is already being utilized by NVIDIA Research, NVIDIA DRIVE, and NVIDIA Omniverse teams. Notable applications include:
Surface Reconstruction: Neural Kernel Surface Reconstruction (NKSR) leverages fVDB to reconstruct high-fidelity surfaces from large point clouds.
Generative AI: XCube combines diffusion generative models with sparse voxel hierarchies, enabling the generation of 3D scenes with high spatial resolution.
NeRFs: NeRF-XL uses fVDB to distribute neural radiance fields across multiple GPUs for large-scale 3D rendering.
Future Developments
NVIDIA plans to integrate fVDB functionality into NVIDIA NIM microservices, enabling developers to incorporate fVDB into Universal Scene Description (OpenUSD) workflows within NVIDIA Omniverse.
Upcoming NVIDIA NIM microservices include fVDB Mesh Generation, fVDB Physics Super-Res, and fVDB NeRF-XL, which will generate OpenUSD-based geometry using Omniverse Cloud APIs.
Conclusion
Developed by NVIDIA, fVDB is a groundbreaking deep-learning framework for sparse, large-scale spatial intelligence. It builds on OpenVDB to enable applications such as digital twins, neural radiance fields, and 3D generative AI.
For more details, visit the official NVIDIA announcement.
Image source: Shutterstock
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