Shared Pose Tracking for XR Re-Entry and Occlusion Gaps
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Solution Overview
Problem
XR devices face challenges in accurately tracking objects, particularly when they exit and re-enter the camera field of view, leading to inconsistencies in rendering virtual content.
Innovation Solution
XR devices share 6DOF pose data with each other to enhance tracking, using techniques like SLAM and VIO, and synchronize spatial reference systems to maintain accurate tracking even when objects are outside the camera view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If XR devices use only camera images to track objects, then device complexity is reduced, but tracking reliability deteriorates when objects exit and re-enter the camera field of view
Solution Approach 1:
The patent combines data from multiple XR devices to establish and maintain pose information. When one device loses track of an object, another device's data compensates, ensuring continuous tracking reliability without requiring each individual device to be overly complex
Solution Approach 2:
The system performs preliminary pose estimation using available camera images before objects exit the field of view, and maintains this pose information in memory. This preliminary action allows the system to quickly re-acquire tracking when objects re-enter without requiring complex real-time analysis
2Measurement precision
If multiple XR devices share pose data to improve tracking, then tracking precision is improved, but device complexity and computational resources increase
Solution Approach 1:
The patent divides the tracking task across multiple XR devices, where each device contributes its observational data. This segmentation allows the system to achieve higher precision through data fusion while keeping individual device complexity manageable
Solution Approach 2:
The system uses an intermediary data structure (pose information stored in memory) that mediates between multiple device inputs. This intermediary layer simplifies the integration of data from multiple sources by providing a standardized format for pose representation
3Stability of the object's composition
If XR devices continuously capture images to maintain tracking, then tracking consistency is improved, but energy consumption and computational load increase
Solution Approach 1:
Instead of continuous image capture, the system uses periodic sampling combined with pose estimation algorithms that can maintain tracking between samples. This periodic approach reduces energy consumption while preserving tracking consistency through intelligent data utilization
Solution Approach 2:
The system uses previously captured images and estimated pose information to maintain tracking during periods when new image capture is reduced. The existing data serves itself to maintain tracking consistency, reducing the need for constant new data acquisition
Data Source
AI summary
Examples disclosed herein relate to the use of shared pose data in extended reality (XR) tracking. A communication link is established between a first XR device and a second XR device. The second XR device is worn by a user. The first XR device receives pose data of the second XR device via the communication link and captures an image of the user. The user is identified based on the image and the pose data.