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A Method for Point Cloud Accuracy Analysis Based on Intensity Information.

Siyuan Li¹, Dehua Zheng¹, Dongjie Yue¹

¹School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China.

Sensors (Basel, Switzerland)

|November 25, 2023

Summary

This summary is machine-generated.

Understanding laser scanning intensity is key to improving point cloud accuracy. This study reveals how surface reflectance and raw intensity data can be used to assess and enhance scanner performance for high-precision 3D measurements.

Keywords:

intensity information point cloud accuracy precision analysis

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Published on: March 6, 2019

Area of Science:

Geomatics Engineering
Metrology
Computer Vision

Background:

Three-dimensional laser scanning is crucial for high-precision applications.
Point cloud accuracy is significantly influenced by intensity information.
Assessing the relationship between intensity and accuracy is vital for data reliability.

Purpose of the Study:

To investigate the correlation between point cloud accuracy and two types of intensity information.
To develop methods for assessing point cloud accuracy using intensity data.
To evaluate the applicable scopes of these assessment methods.

Main Methods:

Analysis of percentage intensity using the Lambertian reflectance model.
Correlation analysis between Lambertian circle fitting radius and ranging error.
Development of a single-point error ellipsoid model based on raw reflectance intensity.

Main Results:

Lambertian circle fitting radius is inversely correlated with scanner ranging error.
Modifying object surface characteristics improves point cloud precision.
Raw intensity-based ranging error model is limited to non-specular reflectance data.
Single-point error ellipsoid model accurately assesses individual point measurement errors.

Conclusions:

Both percentage intensity and raw intensity-based models can evaluate scanner performance and point cloud accuracy.
The findings provide reliable data support for high-precision 3D laser scanning applications.
Surface characteristic modification is a viable strategy for enhancing data precision.