The history of Omniverse can be traced back to 2019, which essentially transforms some traditional procedural 3D production processes into collaborative designs across applications and devices, particularly important in industrial applications.
① Historical Development: The history of Omniverse can be traced back to 2019, when NVIDIA proposed the concept of a 3D real-time collaboration platform. Its launch is closely related to the Turing architecture introduced by NVIDIA in 2018, which first provided the RTX foundation, crucial for real-time ray tracing, and laid the hardware foundation for Omniverse. The development of Omniverse reflects NVIDIA's grasp of the focus on the development of 3D design and simulation technology in the industrial sector against the backdrop of rapid GPU development.
② Product Definition: Omniverse is defined as a modular API and microservices development platform, which essentially transforms some traditional procedural 3D production processes into collaborative designs across applications and devices. In traditional 3D production processes, such as creating a character like Sun Wukong, it requires multiple stages from basic modeling to glossiness, color, etc., involving dozens of different applications. If modifications are needed for the character, such as adjusting the beard or hair, one must start over from the initial steps, leading to inefficiencies in the entire production process. This is also why animation production companies like Disney require a large workforce, yet production efficiency remains low.
③ Core Product: The core of Omniverse lies in building a platform that enables seamless connections and real-time collaboration between different software. This platform allows users to synchronize design steps across different devices and software, achieving real-time updates and sharing. The core changes lie in the evolution of the architecture and the enhancement of physical simulation capabilities, especially after 2021, when the industrial sector began to gradually adopt Omniverse. This is mainly because it can provide a simulation environment that complies with all physical laws, including forces and visuals, which is a key aspect of simulating the entire real world, particularly important for applications requiring complex environmental interactions, such as robotics and intelligent driving.
The core of physical AI is to combine artificial intelligence technology with the laws of the physical world to achieve more accurate and efficient simulation and data generation. Jensen Huang mentioned that the next generation of AI is physical AI, which must comply with the laws of the physical world. The first layer is to create a platform that adheres to physical laws, allowing for simulation on it; the second layer is to generate a large amount of physical AI data for use. Currently, the core tools for robotics and intelligent driving are NVIDIA's Omniverse.
Specifically, the development of physical AI can be divided into two key steps: ① First, it becomes a core simulation tool. Physical AI, as a core simulation tool, ensures that all force simulations and physical scenarios conducted on the platform must comply with the laws of physics. This is particularly important for industrial applications, as all data and validations must be based on physical laws, thereby reducing the need for physical testing and allowing all scenarios to be run through directly in the simulation environment, achieving 100% scene restoration and accelerating the development process of intelligent driving and robotics
Secondly, the deeper application of physical AI is to generate a large amount of data that conforms to the physical world through simulation. This is particularly important in the field of intelligent driving, as high-quality data is a key bottleneck in the development of intelligent driving, including Tesla's FSD, which uses a large amount of real-world data and then feeds it back to its own model to make it more accurate. In the future, physical AI will generate a vast amount of data that conforms to the physical world through models, such as vectors and physical points, which can be used to train edge-side models, such as those in vehicles or control algorithms for robots, achieving real-time perception and feedback.
In NVIDIA's vision for the future, the development of robotics relies on three core computers. One is used for training AI, one is for controlling the testing AI in the physical simulation environment, and one is a simulation environment computer installed inside robots or smart vehicles, supporting physical AI algorithms. One of the currently applied scenarios is to verify the reliability of program logic in the simulation environment; the second is to obtain data that is difficult to acquire from the real world to continuously train AI models. Currently, many large companies are adopting this approach. From a software perspective, Ansys, a leading company in the simulation field, offers simulation products that can also be accessed through NVIDIA's Omniverse. With Ansys's physical solvers aimed at cameras, LiDAR, and radar sensors, it enhances the high-fidelity and scalable 3D environment of NVIDIA DRIVE, which is crucial for the development of autonomous driving systems. In this way, all data during the driving process can be fed back in real-time for decision-making, while generating more similar data to simulate more scenarios, accelerating the improvement of training effects and breaking through the bottleneck of data acquisition.
NVIDIA's significant investment in Omniverse indicates that its future computing power will primarily focus on large model AI generation, robotics, and intelligent driving. We believe that the current product maturity is already very high, and overseas automakers and robotics manufacturers have begun to utilize Omniverse to enhance factory efficiency and intelligence levels, with clear trends in industry changes. Considering that a large amount of data simulation and verification is needed before deploying robots, Omniverse, by digitizing the real physical world, has become an indispensable link. At the same time, the level of intelligence is at a critical juncture, and we look forward to more Chinese companies connecting to the Omniverse platform to enhance production efficiency and intelligence levels.
This article is sourced from Shenwan Hongyuan's Dai Wenjie (SAC certification number: A0230522100006) and Fan Xiapei (SAC certification number: A0230523080004), published on January 6, titled &#34;Nvidia's Next Soft Core Omniverse; How Intelligence Becomes Purchasing Power,&#34; with some edits by Wall Street Insights.
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NVDA

NVIDIA's Omniverse is a modular API and microservices development platform designed to transform traditional 3D production processes into collaborative design across applications and devices. Since its launch in 2019, Omniverse has relied on the RTX foundation of the Turing architecture to enhance the efficiency of industrial 3D design and simulation. Its core lies in achieving seamless connectivity and real-time collaboration between different software, making it particularly suitable for applications involving complex environmental interactions such as robotics and intelligent driving

- Omniverse, introduced by NVIDIA in 2019, transforms traditional 3D production processes into collaborative designs across applications and devices, crucial for industrial use.  
- It serves as a modular API and microservices platform, enabling seamless connections and real-time collaboration, particularly benefiting industries requiring complex simulations.  
- NVIDIA's significant investment in Omniverse indicates a focus on AI generation, robotics, and autonomous driving, with expectations for increased efficiency and intelligence in production processes.

NVIDIA's next soft core - Omniverse