3D Object Recognition via 2D Cross-Sectional Slices
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Solution Overview
Problem
Current methods for identifying and measuring objects in 3D space, such as cellular towers, are time-consuming and computationally expensive due to the complexity of pattern matching and feature extraction in 3D models, especially when using manual tracing and existing search algorithms like kd-trees and quad trees, which are not optimized for 3D pattern recognition.
Innovation Solution
A system utilizing 2D cross-sectional slices of 3D models, generated through photogrammetry and lidar, with machine learning and pattern recognition to identify critical components, correlating them with real-world standards, and generating reports for object recognition and anomaly detection, reducing the dimensionality of feature sets and automating the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tracing and 3D pattern matching are used to identify objects in 3D models, then measurement precision is improved, but productivity deteriorates due to time-consuming manual processes
Solution Approach 1:
The 3D model is segmented into multiple 2D cross-sectional slices at different elevations. Each slice is independently processed through pattern recognition, which simplifies the identification task compared to analyzing the entire 3D model at once. The slices are then integrated to reconstruct complete object information, achieving both accuracy and efficiency.
Solution Approach 2:
The invention transforms the 3D object recognition problem into a series of 2D pattern recognition problems by creating cross-sectional slices. This dimensionality reduction from 3D to 2D simplifies the computational complexity while maintaining measurement precision through the integration of multiple slice data.
2Difficulty of detecting and measuring
If 3D pattern recognition algorithms are used to identify objects in 3D space, then object detection capability is improved, but device complexity worsens due to computational requirements
Solution Approach 1:
The complex 3D pattern recognition task is divided into simpler 2D pattern recognition tasks for each cross-sectional slice. This segmentation reduces the computational burden on the processing system while maintaining detection capability through the aggregation of results from multiple slices.
Solution Approach 2:
By converting 3D pattern recognition into 2D cross-sectional analysis, the invention reduces the dimensionality of the data processing requirement. This lowers the computational complexity and device requirements while preserving object detection capability through multi-elevation analysis.
3Speed
If existing search algorithms like kd-trees and quad trees are used for 3D space search, then search speed is improved, but adaptability deteriorates because they are not optimized for 3D pattern recognition
Solution Approach 1:
The invention transforms the search problem from 3D space to 2D cross-sectional planes. This allows the use of optimized 2D pattern recognition algorithms on each slice while maintaining efficient search performance. The adaptability for pattern recognition is improved because 2D algorithms are better suited for the sliced data structure.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces the time and computational cost of identifying and measuring 3D objects by using 2D cross-sections to automate the recognition of tower-mounted equipment, enabling efficient identification and reporting of anomalies, and facilitating remedial actions.
Implementation Method 1
using photogrammetry, lidar, and other suitable sensor technique to generate a 3D mesh/reality model
Implementation Method 2
using photogrammetry, lidar, and other suitable sensor technique to generate a 3D mesh/reality model
Data Source
AI summary
A reality-based model object recognition system using cross-sections includes using photogrammetry to obtain various views of a 3-dimensional (3D) object (e.g. 3D model, 3D reality model, mesh, etc.). The process then generates 2-dimensional (2D) slices, i.e. cross-sections, of the 3D object at various elevations and angles. The relation between the slices is critical for identification. The 2D slices are used as building blocks for automatic recognition and identification and location (e.g. x,y,z+angle) of a real-world equipment mounted on the 3D object and identifying any anomaly in the equipment so that remedial action may be ordered, if needed.