AR Annotation Refinement for Accurate Virtual Object Alignment
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Solution Overview
Problem
Current augmented reality applications face challenges in accurately aligning virtual object annotations with real-world environments, leading to mislocation of virtual objects and annotations due to changes in the environment, such as design updates or differences in vehicle models, which can confuse users and potentially result in errors or damage.
Innovation Solution
An automated mechanism that refines the location of virtual object annotations by correlating points in the virtual object model with the real-world environment using a model tracking algorithm, evaluating the accuracy of initial estimates, and performing a refinement operation to align annotation points with the correct locations in the real-world environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If virtual object annotations are initially placed without refinement, then the system is simpler and faster, but the accuracy of annotation placement deteriorates due to environmental changes
Solution Approach 1:
The system performs preliminary actions by establishing initial estimates of annotation point locations using pre-existing models and reference data. This preliminary placement enables the system to function immediately while maintaining the foundation for subsequent refinement operations, resolving the contradiction between immediate functionality and ultimate accuracy.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring the alignment between virtual annotation points and real-world reference points. When misalignment is detected due to environmental changes, the system automatically initiates refinement operations using the misalignment information to correct the annotation locations, thereby maintaining high accuracy without requiring complete system redesign.
2Measurement precision
If manual refinement of annotation points is performed, then the accuracy can be improved, but the time required and operational complexity increase
Solution Approach 1:
The system performs self-service by automatically detecting misalignments between virtual and real-world annotation points and initiating refinement operations without human intervention. The automated algorithm uses the misalignment data to recalculate and adjust annotation locations, eliminating the need for manual correction while maintaining high accuracy and reducing time loss.
Solution Approach 2:
The system changes parameters such as annotation point coordinates, transformation matrices, and alignment thresholds to optimize the balance between accuracy and processing time. By dynamically adjusting these parameters based on the degree of misalignment and computational resources available, the system achieves high precision annotation placement without excessive time consumption.
3Productivity
If the system uses pre-existing models for annotation placement, then the process is faster, but adaptability to environmental changes deteriorates
Solution Approach 1:
The system transitions from static pre-existing models to dynamic adaptive models that continuously update based on real-world observations. The refinement operations modify the annotation point locations and model parameters in response to detected environmental changes, enabling the system to maintain both speed and adaptability by building upon pre-existing models rather than replacing them entirely.
Solution Approach 2:
The system uses pre-existing models as a preliminary foundation for rapid annotation placement, then applies refinement operations to adapt to specific environmental conditions. This two-stage approach preserves the speed benefits of pre-existing models while achieving the adaptability needed for changing environments through subsequent automated adjustments.
Data Source
AI summary
Mechanisms are provided for generating an augmented reality representation of a real-world environment. An augmented reality (AR) system receives a captured digital image of the real-world environment and generates an initial estimate of a candidate point specifying an estimated location of an annotation point of a virtual object model within the captured digital image of the real-world environment. An accuracy of the initial estimate is calculated based on a function of characteristics of the annotation point and a function of characteristics of the candidate point and, in response to the evaluation of accuracy indicating that the initial estimate is not accurate, an annotation point location refinement operation is performed to generate a refined candidate point for aligning the annotation point with the captured digital image of the real-world environment. An AR representation of the real-world environment is generated based on the refined candidate point.