Depth-Based Annotation Interface for Spatially Fixed AR Markup
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for augmenting media, such as adding annotations or virtual objects, are cumbersome and inefficient, requiring multiple inputs and causing cognitive burden on users, especially in battery-operated devices.
Innovation Solution
The system utilizes depth data to maintain a fixed spatial relationship between augmentations and physical environments, allowing for intuitive annotation and virtual object placement, and supports shared annotation sessions across devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional methods are used for augmenting media, then annotations can be added to media, but the process is cumbersome and requires multiple inputs increasing cognitive burden
Solution Approach 1:
The system performs preliminary actions by capturing depth data and establishing spatial relationships between camera views and physical environments before the user needs to add annotations. The depth information is preprocessed to create a spatial map that automatically associates annotations with physical objects, eliminating the need for users to manually track and re-locate objects across different camera views.
Solution Approach 2:
Depth data serves as an intermediary between the camera views and the physical environment. This intermediary layer automatically establishes correspondences between images captured at different times and locations, allowing the system to bridge the gap between current camera view and previously captured media without requiring manual user intervention to re-locate objects.
2Productivity
If annotations are added to stored media, then augmentation is possible, but it takes time and wastes energy
Solution Approach 1:
The system performs self-service by automatically using depth data to establish spatial relationships and locate annotations without requiring active user participation. The depth information is processed automatically to determine where annotations should appear in current camera views, eliminating the need for users to manually search through stored media or re-locate objects, thereby reducing both time and energy consumption.
3Measurement precision
If depth data is used to maintain spatial relationship, then annotation location accuracy is improved, but processing requirements increase
Solution Approach 1:
The processing system is segmented into distinct modules: depth data capture, spatial relationship establishment, annotation association, and display rendering. By dividing the complex processing into separate functional segments, each module can be optimized independently and processed more efficiently, reducing overall computational burden while maintaining precision.
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
A computer system displays a representation of a field of view of one or more cameras that is updated with changes in the field of view. In response to a request to add an annotation, the representation of the field of view of the camera(s) is replaced with a still image of the field of view of the camera(s). An annotation is received on a portion of the still image that corresponds to a portion of a physical environment captured in the still image. The still image is replaced with the representation of the field of view of the camera(s). An indication of a current spatial relationship of the camera(s) relative to the portion of the physical environment is displayed or not displayed based on a determination of whether the portion of the physical environment captured in the still image is currently within the field of view of the camera(s).