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Aerial LiDAR Data Augmentation for Direct Point-Cloud Visualisation.

Ciril Bohak1, Matej Slemenik1, Jaka Kordež1

  • 1Faculty of Computer and Information Science, University of Ljubljana, Ljubljana 1000, Slovenia.

Sensors (Basel, Switzerland)
|April 12, 2020
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This study enhances aerial LiDAR point-cloud visualizations by fusing vector map data. The pipeline adds missing terrain, water, and building data, improving visual appeal and data completeness.

Area of Science:

  • Geospatial Science
  • Computer Vision
  • Remote Sensing

Background:

  • Direct visualization of aerial LiDAR scans often suffers from incomplete data due to acquisition limitations.
  • Missing elements like water surfaces, building walls, and terrain features reduce visualization quality and appeal.

Purpose of the Study:

  • To improve the visual quality and data completeness of aerial LiDAR point-cloud rendering.
  • To develop a processing pipeline for augmenting incomplete LiDAR data using external vector map information.

Main Methods:

  • A point-cloud processing pipeline was developed using data fusion techniques.
  • Vector maps of water surfaces and building outlines were used to add missing points.
  • Color information and point normals were calculated for enhanced visualization.
Keywords:
LiDARpoint-cloud visualisationpoint-cloudsterrain reconstructionwater surface reconstruction

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Main Results:

  • The pipeline successfully augmented LiDAR data with missing features, including water surfaces, building walls, and terrain.
  • The addition of color and point normals significantly improved the visual appeal of the rendered point clouds.
  • The approach was validated on the Slovenian LiDAR dataset.

Conclusions:

  • Data fusion with vector maps is an effective method for enhancing aerial LiDAR point-cloud visualizations.
  • The proposed pipeline addresses common data deficiencies in LiDAR scans, leading to more comprehensive and visually appealing results.