3D Point Cloud Facility Detection via Reflection Intensity
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Solution Overview
Problem
Existing technologies struggle to automatically detect a target facility from three-dimensional point cloud data, often resulting in the creation of unnecessary three-dimensional models of surrounding structures like trees and street lamps.
Innovation Solution
Utilizing a three-dimensional laser scanner that measures not only the reflection position but also the reflection intensity of laser light, the method clusters point clouds by density and uses reflection intensity changes to differentiate between artificial and natural objects, thereby identifying the target facility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If three-dimensional models are created for all detected structures, then comprehensive modeling coverage is improved, but the ability to automatically identify target facilities deteriorates
Solution Approach 1:
The patent utilizes changes in reflection intensity as a distinguishing parameter to differentiate between artificial facilities and natural objects. By analyzing the reflection intensity characteristics of point clouds, the system automatically identifies target facilities without requiring manual selection, thus achieving both comprehensive modeling and automatic detection.
2Extent of automation
If reflection intensity measurement is added to the laser scanner, then automatic target facility detection is improved, but device complexity increases
Solution Approach 1:
The patent applies multi-functionality by utilizing the reflection intensity measurement capability of the laser scanner for both geometric modeling and automatic facility detection. The same hardware component (laser scanner) performs multiple functions: capturing spatial information and providing material surface characteristics, thereby avoiding additional hardware complexity while achieving automatic detection.
3Measurement precision
If clustering algorithms are applied to point clouds, then structure extraction accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the point cloud data into distinct clusters based on spatial proximity and reflection intensity characteristics. This segmentation allows the system to process and analyze only the relevant portions of the point cloud, improving extraction accuracy while managing processing time through efficient data organization and analysis.
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 enables accurate and automatic detection of target facilities by distinguishing between artificial and natural objects based on reflection intensity patterns, reducing unnecessary model creation and improving precision.
Implementation Method 1
a three-dimensional laser scanner can measure not only a reflection position of laser light but also the reflection intensity of the laser light
Data Source
AI summary
Provided is a device and a method for extracting point clouds of structures from point clouds by clustering a point cloud in which each point represents three-dimensional coordinates, and detecting a target facility from the structures using reflection intensities of the extracted point clouds of the structures.