Secure Spatial Mapping, Recognition and Alignment for the XR Digital Thread
Uses distributed GPUs for graphics heavy computing for high-fidelity rendering without time-consuming polygon reduction, and wirelessly streaming the solution to headsets, mobile phones, and tablets. (100s of Million polygon)
Achieves accurate 3D spatial mapping with high fidelity 3D scene reconstruction, scene segmentation, and 3D object recognition using 3D computer vision and deep learning-based AI running on discrete GPUs on the server. This enables precise fusion of the real and virtual worlds, and persistent overlay of real objects and their digital twins with millimeter precision using custom 3D object tracking.
Applies gaming tools and concepts in a cloud native environment that allows for agile, secure, and rapid development, deployment, and operations on the cloud/on-premises. GridRaster uses Kubernetes for deployment and scaling. It follows a CI/CD pipeline for deployment of its services into the cluster following the best practices and CNCF graduated projects. This enables loosely coupled systems that are resilient, manageable, and observable, and future proof.
GridRaster’s open architecture API-based approach enables a frictionless onboarding and seamless integration with existing content formats and provides future proof cross-platform support. This also allows the platform to integrate with other systems to share data and allow for interoperability. GridRaster solution uses standard APIs to input the customer content in their native format (Unity, Catia, AutoCad, etc.) and converts them to the correct format to perform the computation on the cloud. Sensor data from the devices – camera, depth, IMU – are also integrated into the software using standard APIs.
GridRaster combines the best of the gaming and traditional simulations to provide a massive multi-user and multi-platform ultra-realistic large world simulations in AR,VR and MR. It provides a cloud-based agile and secure deployment and operation that distributes complex computations across compute server nodes and handles scaling in real time using Kubernetes.
Please set your twitter API settings from here element pack settings to show your map correctly.
