3D Contour Model Generation for AR Object Tracking
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Solution Overview
Problem
The manual generation of contour models for augmented reality systems is complex and time-consuming, making it difficult to efficiently determine data models for superposing with images of real objects in object tracking processes.
Innovation Solution
A method using a three-dimensional CAD model to generate synthetic two-dimensional views, extract relevant edges, and transform them into a three-dimensional contour model, allowing for semi-automatic or fully automatic generation of data models for contour-based object tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual generation of contour models is used, then accuracy of object tracking is improved, but time consumption and complexity increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-generating multiple synthetic two-dimensional views of the object from a three-dimensional CAD model before the actual tracking process. These pre-generated views with extracted edge information are stored and can be quickly retrieved during tracking, eliminating the need for manual contour model generation while maintaining high accuracy.
Solution Approach 2:
The patent uses copying by creating synthetic two-dimensional representations (copies) of the three-dimensional object model from different viewing angles. These synthetic copies contain pre-extracted edge information that can be directly used for tracking, replacing time-consuming manual contour extraction while preserving geometric accuracy.
2Measurement precision
If manual generation of contour models is used, then tracking accuracy is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The system applies self-service by automatically generating synthetic views and extracting edge information from the three-dimensional CAD model without requiring manual intervention. The computer system performs all processing steps autonomously, from rendering synthetic views to detecting edges and creating the contour model, thereby reducing operational complexity while maintaining accuracy.
Solution Approach 2:
The patent replaces manual mechanical operations with automated computational processes. Instead of manually creating contour models, the system uses computer-based rendering, edge detection algorithms, and automated processing to generate the same or better results, thereby reducing complexity and improving consistency.
3Productivity
If automatic generation using synthetic views is implemented, then productivity is improved, but measurement precision may deteriorate
Solution Approach 1:
The patent applies parameter changes by systematically varying viewing parameters (angles, positions, lighting conditions) when generating multiple synthetic two-dimensional views of the object. This comprehensive parameter sampling ensures that all relevant edge features are captured from different perspectives, maintaining high accuracy while enabling automatic processing.
Solution Approach 2:
By pre-generating comprehensive synthetic views with various parameters before tracking, the system ensures that accurate edge information is already available in multiple configurations. This preliminary computation with optimized parameters achieves both high speed during actual tracking and maintains measurement precision through thorough pre-processing.
Data Source
AI summary
A data model which is designed for being superposed with an image of a real object in an optical object tracking process is determined by the following steps: providing a three-dimensional CAD model (10) for representing the real object, and thereafter there are different synthetic two-dimensional views (31 to 34) of said CAD model (10) generated. Each generated view (31 to 34) is subjected to edge extraction for determining at least one extracted edge (38, 39) in the respective view, with the edges (38, 39) extracted from said respective views (31 to 34) being transformed to a three-dimensional contour model (85, 91) corresponding to said data model to be determined. Permits rapid and efficient generation of a contour model as a data model intended for being superposed with an image of a real object.