3D Surface Modeling for Location-Tracked AR Alignment
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Solution Overview
Problem
Existing augmented reality (AR) systems struggle to seamlessly integrate computer-generated enhancements with real-world images, requiring complex image capture and matching processes to achieve a unified perspective.
Innovation Solution
A method for creating custom 3D models of static features or objects using a two-step process involving Simultaneous Localization And Mapping (SLAM) tracking and 3D mesh generation, aligned with each other, allowing users to create AR experiences tailored to specific locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex image capture and matching processes are used to integrate computer-generated enhancements with real-world images, then the quality and accuracy of AR rendering is improved, but the device complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing images to identify and extract planar surfaces before AR rendering. The image capture process includes preliminary steps to detect surfaces, determine their properties (flatness, orientation), and prepare them for augmentation. This preliminary processing simplifies subsequent matching and rendering operations by pre-organizing the data structure and identifying valid augmentation targets.
Solution Approach 2:
The image processing is segmented into distinct functional modules: surface detection module that identifies planar regions, surface property analysis module that characterizes detected surfaces, and AR rendering module that applies enhancements only to validated surfaces. This segmentation allows each module to specialize in specific tasks, improving overall system efficiency and accuracy while managing complexity through modular design.
2Reliability
If comprehensive surface detection and analysis is performed to identify suitable AR targets, then the reliability of AR augmentation is improved, but the processing time and computational resources increase
Solution Approach 1:
The system applies local quality analysis by evaluating specific local properties of image regions rather than processing the entire image uniformly. Surface detection focuses on identifying planar regions with specific characteristics (flatness thresholds, orientation ranges), and only these locally-identified regions undergo further analysis and augmentation. This localized approach improves reliability by being selective about augmentation targets while reducing overall processing time by excluding non-candidate regions.
Solution Approach 2:
The system uses parameter-based filtering to quickly eliminate unsuitable regions for AR augmentation. By defining specific parameters (surface flatness, orientation, size thresholds) and comparing detected regions against these parameters, the system efficiently identifies valid augmentation targets. Parameter changes allow dynamic adjustment of detection sensitivity and can optimize the balance between reliability and processing speed based on computational resources available.
Data Source
AI summary
The present disclosure provides a method for creating a 3D model of a reference surface. The method includes capturing, using a capture device, a plurality of data points on the reference surface, determining a position and an orientation of the capture device related to the capture of the plurality of data points, creating a 3D data representation of the reference surface based on the plurality of data points, creating a location tracking data representation of the reference surface based the plurality of data points on the reference surface and the position and the orientation of the capture device, and creating the 3D model of the reference surface based on the 3D data representation and the location tracking data representation of the reference surface.