Nvidia enhances OpenUSD to drive wider industry adoption

Nvidia has announced major updates to its Universal Scene Description (OpenUSD) framework, targeting expanded adoption in robotics, industrial design, and engineering.

The updates include NIM microservices for AI models that can generate OpenUSD language to answer user queries, produce OpenUSD Python code, apply materials to 3D objects, and understand 3D space and physics to accelerate digital twin development.

NIMs, or Nvidia inference microservices, are a set of tools that makes it easier for businesses to use AI models in their applications, across clouds, data centers, and workstations.

Additionally, the company announced new USD connectors for robotics and industrial simulation data formats, along with developer tools, that will let users stream massive, fully Nvidia RTX ray-traced datasets to Apple Vision Pro.

Through the new frameworks, built on the Nvidia Omniverse platform, more industries can develop applications for visualizing industrial design and engineering projects, the chipmaker said in a statement.

“Until recently, digital worlds have been primarily used by creative industries,” Rev Lebaredian, VP of Omniverse and simulation technology at Nvidia, said in the statement. “Now, with the enhancements and accessibility Nvidia NIM microservices are bringing to OpenUSD, industries of all kinds can build physically based virtual worlds and digital twins to drive innovation while preparing for the next wave of AI: robotics.”

The iPhone maker, Foxconn, which operates over 170 factories globally, is already utilizing Nvidia’s computing platform, including NIM microservices and Omniverse, to develop a digital twin of a factory, Nvidia added.

Benefits to industrial customers

Creating hyper-realistic virtual worlds and digital twins is crucial for simulating environments and optimizing industrial processes. If Nvidia’s solutions prove successful, the industry could undergo a significant transformation.

“By integrating these cutting-edge technologies, developers can visualize industrial designs and engineering projects more accurately, accelerating advancements in robotics and AI applications,” said Thomas George, president of Cybermedia Research. “For instance, the automotive sector could see a significant boost in efficiency, with potential maintenance effort savings of up to 10% annually.”

In sectors like robotics, OpenUSD’s accurate simulations in various environments can improve AI model training and real-world operation, according to Manish Rawat, a semiconductor analyst at Techinsights.

“This capability fosters the development of sophisticated robotic systems through enhanced visualization and interaction in virtual environments,” Rawat said. “Companies adopting OpenUSD early will gain a competitive edge, especially those leveraging digital twins and simulations.” 

The digital twins ecosystem to grow

Analysts expect Nvidia’s move to increase the use of digital twins in manufacturing, automotive testing and development, and robotics. It also places Nvidia and its partners at the forefront of the digital twin market, according to Rawat.

“As the first to introduce generative AI models for OpenUSD, Nvidia secures a significant market advantage, attracting early adopters and setting industry benchmarks,” Rawat said.

Another significant factor that could expand the ecosystem and benefit Nvidia is its collaboration plans. In addition to integrating Apple Vision Pro, Nvidia is extending its partnership with Siemens to facilitate more industrial workloads using OpenUSD. “The synergy between these platforms is expected to drive widespread adoption of simulation technologies across various sectors,” George said. “Apple Vision Pro, for example, empowers users to visualize complex simulations through augmented reality, facilitating improved decision-making in manufacturing and design environments. Siemens will also benefit significantly from OpenUSD’s robust framework, which enhances its simulation capabilities and enables more precise and efficient modeling of industrial processes.”