Dynamic AR Experiences Through Real-World Semantic Detection
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Solution Overview
Problem
Existing augmented reality systems lack the ability to provide dynamic and interactive experiences that leverage real-world semantics and physical shapes in real-time, limiting user engagement and immersion.
Innovation Solution
An AR experience system that utilizes computer vision and machine learning to understand the real-world environment, allowing for the creation of interactive experiences by detecting objects and applying AR elements based on user interactions and environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional augmented reality systems are used, then basic AR overlay functionality is provided, but dynamic and interactive experiences leveraging real-world semantics and physical shapes in real-time cannot be achieved
Solution Approach 1:
The system performs preliminary semantic understanding and object detection of the real-world environment before generating AR experiences. By pre-processing and understanding the physical space, objects, and their relationships, the system can dynamically place and interact with virtual objects in real-time without requiring complex real-time computation during user interaction
Solution Approach 2:
The patent introduces semantic understanding as an intermediary layer between the physical world and virtual AR objects. This semantic layer interprets real-world objects, their properties, and relationships, enabling intelligent placement and interaction of AR elements without direct complex computation between sensors and rendering systems
2Productivity
If real-time semantic understanding and object detection are implemented, then dynamic and immersive AR experiences are enabled, but computational requirements and processing time increase
Solution Approach 1:
The system segments the AR experience generation into distinct modules: environmental scanning, semantic understanding, object detection, AR element selection, and rendering. Each module processes specific aspects independently, allowing optimized resource allocation and parallel processing to reduce overall computational energy consumption while maintaining real-time performance
3Reliability
If AR objects are placed based on semantic understanding of the environment, then user engagement and immersion are enhanced, but measurement and detection complexity increases
Solution Approach 1:
The system implements feedback loops where AR object placement and user interactions are continuously monitored. Based on user engagement metrics and environmental changes, the system adjusts semantic understanding models and object placement strategies to improve accuracy and user immersion over time while reducing detection complexity through learned patterns
Data Source
AI summary
Systems and methods describe an augmented reality (AR) system for generating AR experiences in real-time. The AR experience system receives a selection of an AR experience from an application running on a computer device, displays a first set of textual cues associated with the AR experience on the computer device, receives a first set of image data corresponding to the first set of textual cues, generates a first AR object associated with the AR experience, displays the first AR object on the computer device and a second set of textual cues associated with the AR experience, receives a second set of image data corresponding to the second set of textual cues, and generates a second AR object associated with the AR experience.