3D Data Alignment via Fiducial Marker Registration
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Solution Overview
Problem
Current data generation and analysis techniques face challenges in aligning and comparing multiple three-dimensional (3D) data sets, particularly in identifying and aligning fiducial markers across different data sets representing the same object or set of objects, which is crucial for accurate data analytics and understanding changes over time or under different conditions.
Innovation Solution
The method involves accessing multiple 3D data sets, identifying common fiducial markers, and performing transformations such as rotation, scaling, and translation to align these markers within a threshold distance, allowing for the identification of differences between the data sets without filtering or losing data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple 3D data sets are generated from different sensors and conditions, then data completeness and analytical value are improved, but alignment difficulty and processing complexity increase
Solution Approach 1:
The patent introduces fiducial markers as intermediary reference objects that are placed on the target object before data collection. These markers serve as a common reference framework across all sensor types and imaging conditions, enabling automatic alignment and registration of multiple 3D data sets without complex manual calibration procedures.
Solution Approach 2:
The patent creates a standardized reference copy of the fiducial markers across all data sets. By identifying and matching the same fiducial marker positions in each 3D data set from different sensors, the system establishes a consistent coordinate system that simplifies the integration and comparison of multi-source data.
2Measurement precision
If manual alignment of fiducial markers is performed, then alignment precision can be achieved, but time consumption and operational complexity increase
Solution Approach 1:
The patent implements automatic fiducial marker detection and alignment algorithms that self-correct positions by identifying common markers across data sets. The system autonomously calculates transformation parameters and applies registrations without requiring manual intervention, thereby achieving high precision alignment while minimizing time consumption.
Solution Approach 2:
The patent employs iterative feedback mechanisms where the alignment algorithm continuously refines marker position matching by comparing transformed coordinates across multiple data sets. This feedback loop automatically optimizes alignment precision by adjusting transformation parameters until convergence criteria are met, eliminating the need for time-consuming manual adjustments.
Data Source
AI summary
Techniques are disclosed for aligning fiducial markers that commonly exist in each of multiple different N-dimensional (N-D) data sets. Notably, the N-D data sets are at least three-dimensional (3D) data sets. A first set and a second set of N-D data are accessed. A set of one or more fiducial markers that commonly exist in both those sets are identified. Based on the fiducial markers, one or more transformations are performed to align the two sets. Performing this alignment process results in at least a selected number of the common fiducial markers that exist in the two sets being within a threshold alignment relative to one another.