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Aerial LiDAR Data Augmentation for Direct Point-Cloud Visualisation

Direct point-cloud visualisation is a common approach for visualising large datasets of aerial terrain LiDAR scans. However, because of the limitations of the acquisition technique, such visualisations often lack the desired visual appeal and quality, mostly because certain types of objects are inco...

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Detalles Bibliográficos
Autores principales: Bohak, Ciril, Slemenik, Matej, Kordež, Jaka, Marolt, Matija
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180474/
https://www.ncbi.nlm.nih.gov/pubmed/32276364
http://dx.doi.org/10.3390/s20072089
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author Bohak, Ciril
Slemenik, Matej
Kordež, Jaka
Marolt, Matija
author_facet Bohak, Ciril
Slemenik, Matej
Kordež, Jaka
Marolt, Matija
author_sort Bohak, Ciril
collection PubMed
description Direct point-cloud visualisation is a common approach for visualising large datasets of aerial terrain LiDAR scans. However, because of the limitations of the acquisition technique, such visualisations often lack the desired visual appeal and quality, mostly because certain types of objects are incomplete or entirely missing (e.g., missing water surfaces, missing building walls and missing parts of the terrain). To improve the quality of direct LiDAR point-cloud rendering, we present a point-cloud processing pipeline that uses data fusion to augment the data with additional points on water surfaces, building walls and terrain through the use of vector maps of water surfaces and building outlines. In the last step of the pipeline, we also add colour information, and calculate point normals for illumination of individual points to make the final visualisation more visually appealing. We evaluate our approach on several parts of the Slovenian LiDAR dataset.
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spelling pubmed-71804742020-05-01 Aerial LiDAR Data Augmentation for Direct Point-Cloud Visualisation Bohak, Ciril Slemenik, Matej Kordež, Jaka Marolt, Matija Sensors (Basel) Article Direct point-cloud visualisation is a common approach for visualising large datasets of aerial terrain LiDAR scans. However, because of the limitations of the acquisition technique, such visualisations often lack the desired visual appeal and quality, mostly because certain types of objects are incomplete or entirely missing (e.g., missing water surfaces, missing building walls and missing parts of the terrain). To improve the quality of direct LiDAR point-cloud rendering, we present a point-cloud processing pipeline that uses data fusion to augment the data with additional points on water surfaces, building walls and terrain through the use of vector maps of water surfaces and building outlines. In the last step of the pipeline, we also add colour information, and calculate point normals for illumination of individual points to make the final visualisation more visually appealing. We evaluate our approach on several parts of the Slovenian LiDAR dataset. MDPI 2020-04-08 /pmc/articles/PMC7180474/ /pubmed/32276364 http://dx.doi.org/10.3390/s20072089 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bohak, Ciril
Slemenik, Matej
Kordež, Jaka
Marolt, Matija
Aerial LiDAR Data Augmentation for Direct Point-Cloud Visualisation
title Aerial LiDAR Data Augmentation for Direct Point-Cloud Visualisation
title_full Aerial LiDAR Data Augmentation for Direct Point-Cloud Visualisation
title_fullStr Aerial LiDAR Data Augmentation for Direct Point-Cloud Visualisation
title_full_unstemmed Aerial LiDAR Data Augmentation for Direct Point-Cloud Visualisation
title_short Aerial LiDAR Data Augmentation for Direct Point-Cloud Visualisation
title_sort aerial lidar data augmentation for direct point-cloud visualisation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180474/
https://www.ncbi.nlm.nih.gov/pubmed/32276364
http://dx.doi.org/10.3390/s20072089
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