Biometric Depth Estimation for Shared AR User Distance
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Solution Overview
Problem
Existing augmented reality systems face challenges in accurately determining the distance between users to generate realistic virtual object interactions, particularly when biometric data and positioning data are used to create shared AR scenes.
Innovation Solution
The system employs biometric data, such as the size of a user's face, in conjunction with positioning data to determine the distance between users, using methods like skeleton tracking and image analysis to generate accurate depth estimations for augmented reality experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If biometric data and positioning data are used to determine user distance in AR systems, then depth estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent uses biometric data (face size, body proportions) as an intermediary reference to translate 2D image coordinates into 3D spatial depth information. By introducing known physical dimensions of human features as a mediator, the system can calculate distance without requiring complex dedicated depth sensing hardware, thus improving measurement precision while managing device complexity
Solution Approach 2:
The patent replaces traditional mechanical or optical depth sensing mechanisms (such as time-of-flight sensors, structured light projectors, or stereo cameras) with a computational approach using biometric data and 2D image analysis. This substitution uses information processing instead of physical sensing mechanisms to achieve depth estimation
2Measurement precision
If skeleton tracking and image analysis methods are employed to determine user distance, then depth estimation accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-establishing biometric reference data (standard face sizes, body proportions) and pre-processing skeleton tracking data to extract key anatomical landmarks. By preparing these reference frameworks in advance, the system reduces real-time processing requirements when actual depth estimation is needed, thus improving measurement precision while minimizing processing time loss
Data Source
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AI summary
Method of generating depth estimate based on biometric data starts with server receiving positioning data from first device associated with first user. First device generates positioning data based on analysis of a data stream comprising images of second user that is associated with second device. Server then receives a biometric data of second user from second device. Biometric data is based on output from a sensor or a camera included in second device. Server then determines a distance of second user from first device using positioning data and biometric data of the second user. Other embodiments are described herein.