Environment-Aware XR Content Presentation via Image Classification
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Solution Overview
Problem
Existing XR systems struggle to provide an immersive experience by presenting content that is not dynamically adapted to the user's physical environment, leading to a lack of contextual relevance.
Innovation Solution
A device with an image sensor, display, and processors classifies the physical environment into specific types (e.g., coastal, forest, tundra) based on images and presents corresponding XR content, such as virtual objects and audio, that align with the environment type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If XR content is presented without environmental classification, then the system is simpler and faster, but the contextual relevance and immersion are reduced
Solution Approach 1:
The system performs preliminary environmental classification by capturing images with the image sensor and processing them through the processor to determine environment type before presenting XR content. This advance preparation allows the system to have environment-specific content ready, improving contextual relevance without adding significant runtime complexity.
Solution Approach 2:
The device uses its own image sensor and processor to automatically classify the environment and select appropriate content without requiring external input or manual configuration. The system serves itself by autonomously adapting the XR content presentation based on its own environmental assessment capabilities.
2Loss of information
If environmental classification is implemented, then content relevance is improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential environmental features from captured images that are necessary for classification, rather than processing all image data. The processor identifies key visual cues to determine environment type, extracting only the critical information needed for content selection while discarding redundant data, thus minimizing processing time.
3Adaptability or versatility
If multiple environment types are supported, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system segments the environment classification task into distinct environment types (e.g., indoor, outdoor, natural, urban). Each environment type has its own classification criteria and associated content set. This segmentation allows the processor to use simple decision rules for each category rather than attempting a single complex classification model, managing complexity while supporting multiple environments.
Data Source
AI summary
In one implementation, a method of presenting content is performed by a device including an image sensor, a display one or more processors, and non-transitory memory. The method includes obtaining, using the image sensor, an image of a physical environment. The method includes classifying, based on the image of the physical environment, the physical environment as a particular environment type of a plurality of environment types. The method includes obtaining content based on the particular environment type. The method includes displaying, on the display, a representation of the content in association with the physical environment.