Automatic Multiuser AR Object Placement Across Shared Fields of View
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Solution Overview
Problem
Existing AR systems face challenges in automatically determining optimal placement of shared AR objects that ensure maximum viewability for all users, as manual placement methods are inefficient and prone to occlusions, requiring trial and error and frequent adjustments due to user movements.
Innovation Solution
An AR application analyzes the geometry of AR devices and the environment to determine a placement position for AR objects that maximizes viewability by identifying an intersection region of overlapping fields of view, adjusting for occlusions, and considering object-specific placement preferences, while updating positions based on user changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual placement of AR objects is used, then users can control object position, but the placement efficiency is low and requires trial and error
Solution Approach 1:
The system automatically determines optimal AR object placement positions by analyzing user positions, fields of view, and environmental geometry without requiring manual user intervention. The system serves itself by computing placement positions based on received data from multiple AR devices, thereby eliminating the inefficient trial-and-error manual placement process while maintaining optimal visibility for all users.
2Reliability
If AR objects are placed to be visible to all users, then viewability is improved, but the system complexity increases due to geometry analysis
Solution Approach 1:
The server implements a multi-functional system that simultaneously performs field of view calculation, environmental geometry analysis, occlusion detection, and optimal placement position determination. By consolidating these functions into a single server-based system, the solution achieves comprehensive viewability for all users while managing complexity centrally rather than distributing it across multiple devices.
3Productivity
If automatic placement system is implemented, then placement speed is improved, but the computational requirements increase
Solution Approach 1:
The server acts as an intermediary that receives position and orientation data from multiple AR devices, performs the computationally intensive geometry analysis and placement optimization, then returns the determined placement position to the devices. This mediator approach distributes computational load appropriately, enabling fast automatic placement while managing energy consumption by performing heavy calculations centrally rather than on each resource-constrained AR device.
4Stability of the object's composition
If AR objects are placed near walls or surfaces, then object stability is improved, but the placement flexibility is reduced
Solution Approach 1:
The system dynamically adjusts placement parameters including position coordinates, orientation angles, and distance from surfaces based on real-time analysis of user positions, fields of view, and environmental geometry. By changing these parameters optimally for each specific situation rather than using fixed placement rules, the system achieves both stable placements (when near surfaces) and flexible adaptability (when users are in various positions throughout the environment).
Data Source
AI summary
The present application provides for adjusting placement positions of an AR object that maximizes viewability for AR devices viewing the AR object. The AR application, after initiating an AR session, may receive a request to display the AR object in an AR environment from a number of other AR devices that wish to view the AR object. The position and orientation for each of the AR devices is determined by the AR application, which then determines a respective field-of-view region for each of the AR devices. Based on these respective field-of-view regions, the AR application identifies an intersection region where all of the respective field-of-view regions intersect, and then selects a first placement position for the AR object within this intersection region. The AR object is then placed at a second placement position based on tags and analysis of the physical area.