Snap inc. (20240346775). STATIONARY EXTENDED REALITY DEVICE simplified abstract
Contents
STATIONARY EXTENDED REALITY DEVICE
Organization Name
Inventor(s)
Adrian Bradford of Los Angeles CA (US)
Christopher Cavins of Santa Clarita CA (US)
James Vonk of Long Beach CA (US)
STATIONARY EXTENDED REALITY DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240346775 titled 'STATIONARY EXTENDED REALITY DEVICE
The abstract describes a method and system for creating an extended reality (XR) experience using a stationary device that captures real-world objects in a specified field of view.
- The system receives an image of a real-world object from a stationary camera and uses machine learning to predict tracking information for the object.
- Based on the predicted tracking information, the system overlays XR elements on the image to generate a modified XR experience.
- The machine learning model is trained on a variety of training images depicting real-world objects and their corresponding tracking information.
Potential Applications: - Augmented reality gaming - Virtual tours of real-world environments - Interactive educational experiences
Problems Solved: - Enhancing user engagement with XR technology - Improving the accuracy of object tracking in XR experiences
Benefits: - Immersive XR experiences - Real-time object tracking - Personalized XR content based on real-world objects
Commercial Applications: Augmented Reality Marketing: Leveraging XR technology to create interactive and engaging marketing campaigns for businesses in various industries.
Prior Art: Prior research in machine learning-based object tracking in XR technology can provide insights into the development of this system.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms for object tracking in XR technology to enhance the system's performance.
Questions about XR: 1. How does this system improve upon existing methods of object tracking in XR experiences? 2. What are the potential limitations of using machine learning for predicting tracking information in XR applications?
Original Abstract Submitted
methods and systems are disclosed for generating an extended reality (xr) experience using a statically positioned device. the system receives, from a camera of a stationary device, an image depicting a real-world object, the camera being directed in a stationary manner towards a specified field of view of a real-world environment. the system analyzes the image using a machine learning model to predict tracking information for the real-world object, the machine learning model trained based on a plurality of training images depicting real-world objects in the specified field of view of the real-world environment and corresponding ground-truth tracking information for the real-world objects. the system selects an extended reality (xr) experience from a plurality of xr experiences and overlays one or more xr elements associated with the xr experience on the image based on the predicted tracking information to generate a modified image.
