Neural 3D Content Extension for Automated AR Space Integration
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Solution Overview
Problem
Existing augmented reality (AR) systems require a time- and resource-intensive process to combine traditional media content with the layout of a physical space, involving multiple teams of artists and developers to convert and position AR assets manually.
Innovation Solution
A machine learning model is used to input a physical space layout and anchor content, generating a three-dimensional volume that seamlessly integrates the anchor content with the physical space, allowing for automatic and efficient generation of AR content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional manual processes are used to convert media content into AR assets and position them within physical spaces, then the quality and precision of AR content placement can be maintained through expert judgment, but the process becomes extremely time-consuming and resource-intensive
Solution Approach 1:
The patent replaces the manual mechanical process of artists and developers positioning AR assets with an automated computer vision system. The system uses image processing algorithms to automatically detect physical space features, match AR assets to appropriate locations, and render the augmented reality content without human intervention in the positioning process.
Solution Approach 2:
The system enables self-service automation where the AR content generation process serves itself through automated feature detection, asset matching, and placement algorithms. The computer vision system independently completes the entire workflow from analyzing the physical space to positioning AR assets, eliminating the need for manual team coordination and iterative adjustments.
2Adaptability or versatility
If multiple teams of artists and developers are deployed to manually create and position AR assets, then the quality and adaptability of AR content can be improved, but the complexity of the production process increases significantly
Solution Approach 1:
The patent implements a universal automated system that performs multiple functions previously requiring different specialized teams. The computer vision system simultaneously handles feature detection, asset matching, spatial reasoning, and content rendering, consolidating the capabilities of artists, developers, and technicians into a single multi-functional platform.
Solution Approach 2:
The system introduces an intermediary automated processing layer between the raw media content and the final AR deployment. This intermediary system uses computer vision algorithms to bridge the gap between physical space characteristics and appropriate AR asset selection, automatically adapting content to fit various environments without requiring manual customization for each scenario.
3Manufacturing precision
If manual conversion and positioning processes are used for each piece of media content and physical space, then the precision of AR asset integration can be maintained, but the productivity and throughput of AR content generation decrease
Solution Approach 1:
The patent establishes continuous automated processing where the computer vision system operates without interruption to analyze physical spaces, select appropriate AR assets, and position them accurately. The system maintains continuous useful action by automatically flowing from one processing stage to the next without manual intervention, enabling high-volume AR content generation while preserving placement precision through consistent algorithmic execution.
Data Source
AI summary
Generating augmented reality content includes inputting a first layout of a physical space and a first set of anchor content into a machine learning model; generating, via execution of the machine learning model, a first augmented reality view that includes (i) a first portion of the physical space and (ii) an extension of the first set of anchor content across a second portion of the physical space; and causing the first augmented reality view to be outputted in a computing device.