Virtual Window Repositioning Using Depth and Feature Point Analysis
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Solution Overview
Problem
Augmented reality glasses have a limited field-of-view, making it impossible to present all information simultaneously, especially in remote expert assistance scenarios, where users must manually move virtual windows to avoid obscuring the main work area.
Innovation Solution
A virtual window configuration device and method using depth and feature point detection sensors to analyze images and move virtual windows to suitable positions within the field-of-view, reducing shading and manual operation requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple virtual information windows are displayed simultaneously in augmented reality glasses, then information presentation capability is improved, but field-of-view obstruction increases
Solution Approach 1:
The virtual window automatically adjusts its position dynamically based on real-time detection of depth information and feature points in the real-world scene. The window transitions from static placement to dynamic repositioning, moving to locations that minimize obstruction of the user's field-of-view while maintaining information accessibility.
Solution Approach 2:
The system continuously captures depth information from depth detection sensors and feature point data from feature point detection sensors, processes this feedback information to analyze the real-world scene, and automatically repositions virtual windows based on the analyzed configuration blocks. This closed-loop feedback mechanism ensures optimal window placement that reduces field-of-view obstruction.
2Object-affected harmful factors
If virtual windows are manually repositioned to avoid obstruction, then field-of-view clarity is improved, but user operation complexity increases
Solution Approach 1:
The virtual window repositioning function operates autonomously without requiring user intervention. The system independently detects depth information, identifies feature points, analyzes suitable configuration blocks, and automatically moves virtual windows to optimal positions. This self-service mechanism eliminates the need for users to manually drag and reposition windows, significantly reducing operational complexity.
Solution Approach 2:
The manual mechanical interaction of dragging and dropping virtual windows is replaced by an automated system that uses depth detection sensors and feature point detection sensors to intelligently determine and execute window repositioning. This substitution of mechanical user actions with automated sensing and processing simplifies the user interface and reduces operational burden.
3Loss of information
If virtual windows are placed in the main work area, then information visibility is improved, but work area obstruction increases
Solution Approach 1:
The system transitions from two-dimensional screen-based window placement to three-dimensional spatial positioning of virtual windows in the augmented reality environment. By utilizing depth information and spatial feature points, the system can place windows in three-dimensional space around the user's field-of-view, effectively separating information display from the two-dimensional work area and reducing obstruction.
Solution Approach 2:
The system segments the augmented reality space into multiple configuration blocks based on depth information and feature point analysis. Virtual windows are assigned to specific configuration blocks that are spatially separated from the main work area, allowing information visibility while minimizing interference with work activities.
Data Source
AI summary
A virtual window configuration method includes the following steps. A processor generates a virtual window. A depth detection sensor generates depth information based on an image. The processor analyzes the depth information to generate a depth matrix. The processor finds a depth configuration block in the image using the depth matrix. A feature point detection sensor generates feature point information for the image. The processor analyzes the feature point information to generate a feature point matrix. The processor finds a feature point configuration block in the image using the feature point matrix. The processor moves the virtual window to the depth configuration block or the feature point configuration block.