Imagine a world where machines not only see but understand, and where virtual replicas of real-world entities and processes revolutionize industries. Computer vision and digital twins are at the forefront of this technological revolution. Computer vision enables machines to interpret and understand images and video, much like human vision. The Digital Twin Consortium defines digital twins as virtual representations of real-world entities and processes, synchronized at a specified frequency and fidelity. Computer vision and digital twins have a symbiotic relationship, with the ability to complement each other’s capabilities.
By integrating computer vision capabilities into digital twin environments, organizations can leverage the power of visual data to enhance key outcomes such as the safety of personnel and facilities, operational efficiency, environmental impact and revenue-generating opportunities. This is universally applicable across industries such as manufacturing, smart cities, healthcare, transportation, retail, VR/AR training and more.
One significant aspect in the context of computer vision and digital twins is the emergence of AI inferencing at the edge. AI inferencing can be performed directly on infrastructure at the edge, closer to where data is generated. By performing inferencing at the edge, organizations can achieve real-time responsiveness and reduce latency while helping to ensure privacy and security of sensitive data. To take the benefits of edge inferencing even further, Dell has built a federated analytics architecture that enables organizations to analyze real-time data at dispersed edge locations, while distributing the learnings and insights across locations to eliminate regional bias while improving decision-making and outcomes. In the context of digital twins, edge inferencing enables faster decision-making, immediate feedback and enhanced autonomy within the virtual environment. It allows the digital twin to leverage real-time visual data such as videos and images, analyze it locally and respond in a timely manner, leading to more accurate simulations and more efficient optimization.
We are thrilled to announce the release of the Dell Technologies Validated Design – Computer Vision for Digital Twin Phase 2. This validated design simplifies and speeds up digital twin implementations, taking the guesswork out of building and solutioning, and accelerating your time to value. This validated design builds upon the success of Phase 1, which was covered in a technical white paper and three-part blog series. Phase 1 of the solution focused on the infrastructure deployment, development of 3D models, and generation of synthetic data within an application built on the NVIDIA Omniverse platform using Dell PowerEdge R760xa and NVIDIA L40 GPUs to enhance digital twin projects. The solution showcases the power of combining computer vision and digital twins, providing a comprehensive framework for creating and simulating virtual environments with real-world accuracy.
Now we’ve taken these capabilities a step further with insights on smart city use cases utilizing a geographic information system (GIS). The solution utilizes Esri ArcGIS CityEngine, an advanced 3D modeling software specifically designed for creating and interacting with urban environments and scenarios, using either synthetic or real-world GIS data. The solution offers various ways to generate a 3D scene using GIS data, with simple functions to enrich the 3D scene with details such as streets, terrain, and base maps. This simplifies the cumbersome process for creating 3D scenes from scratch. To enable 3D scene simulation, the scenes can be easily connected to an OpenUSD-based application built on NVIDIA Omniverse. The unidirectional link between Esri ArcGIS CityEngine and applications built by developers on the NVIDIA Omniverse platform provides the flexibility to tweak the model without performing a full re-run to increase efficiency.
The solution also underscores Dell’s comprehensive approach to reducing risk and complexity by thoroughly testing all the components and applications in the stack together in one of Dell’s validation labs. These validated solutions simplify the process by providing design and implementation guides along with sizing tools.
Examples of the CityEngine Get-Map Data Function
Furthermore, this solution demonstrates Dell’s ecosystem-centric approach and highlights the value of Dell infrastructure for implementing scalable, realistic digital twin environments that are simple and efficient to operate and maintain. Examples of this solution implementation would include building AR/VR models for first responders so they can train in the “actual” facility without having to disrupt the occupants. It can also be used by city planners or traffic departments to plan routing during major construction to minimize the impact on commuters or mass transit systems.
Moving forward, Dell will continue to invest in digital twin capabilities and continuously expand the existing design with additional use cases and capabilities.
This Dell Validated Design use case is an example of what can be achieved through the Dell AI Factory with NVIDIA framework. For more information on this phase of the Validated Design, please take a moment to review the new technical white paper. Learn more about this and additional Dell Validated Designs and feel free to contact one of Dell Technologies’ Computer Vision experts.
