Codeless 3D Object Model Anchors from Mesh Faces
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing augmented reality (AR) systems face challenges in accurately identifying real-world assets due to missing, worn, or obscured computer-readable codes on labels, leading to inefficiencies in accessing relevant information.
Innovation Solution
A codeless anchor generation technique for three-dimensional object models, utilizing mesh faces and segments to generate object anchors, enabling direct interaction with real-world assets without relying on visible codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computer-readable codes are placed on labels for AR identification, then information access is enabled, but the labels may be missing, worn, or obscured making identification unreliable
Solution Approach 1:
The patent introduces detectable features as intermediary elements that mediate between the physical object and the AR system. Instead of requiring direct reading of codes on labels, the system detects features (such as geometric patterns, color combinations, or structural characteristics) on or near the object to establish anchors for AR content placement. This intermediary approach eliminates the need for visible codes while maintaining reliable identification.
Solution Approach 2:
The patent creates virtual copies of physical objects by detecting their geometric and visual characteristics. The system generates three-dimensional models and anchors based on detected features rather than requiring direct access to coded information. This copying approach allows the AR system to represent and interact with objects without needing readable labels, thereby improving reliability when labels are absent or obscured.
2Productivity
If codes are required for AR object identification, then information can be accessed, but time is wasted locating and reading worn or hidden labels
Solution Approach 1:
The patent replaces the mechanical/optical process of manually locating and visually reading codes with an automated detection system. The AR device captures images or depth data and automatically identifies objects through computer vision algorithms that detect geometric features, color patterns, or structural characteristics. This substitution eliminates the manual search and reading process, significantly reducing time loss while maintaining efficient information access.
3Adaptability or versatility
If labels with codes are used for asset identification, then AR interaction is enabled, but the system becomes dependent on visible codes which may be obscured by mounting brackets or other labels
Solution Approach 1:
The patent segments the identification process into multiple independent detection channels. Instead of relying on a single code location, the system can detect various types of features (geometric patterns, color combinations, structural characteristics) at different locations on or near the object. This segmentation allows the system to find alternative identification cues when one location is obstructed, maintaining AR interaction capability despite label obstruction by mounting brackets or other labels.
Data Source
AI summary
Techniques include identifying a set of mesh faces, wherein each mesh face included in the set of mesh faces corresponds to different portions of one or more surfaces in a physical space. The techniques further include selecting a subset of mesh faces from among the set of mesh faces, wherein at least a portion of each mesh face included in the subset of mesh faces is within a capture area associated with the physical space. The techniques further include selecting a first mesh face included in the subset of mesh faces that is proximate to a surface of a real-world asset. The techniques further include generating a first mesh segment that includes the first mesh face. The techniques further include generating a first object anchor that is associated with the first mesh segment.