• Reconstruction and Texturing o...

Treffer: Reconstruction and Texturing of 3D Surfaces From Fused Low-Cost Aerial Lidar and Optical Imagery

Title:
Reconstruction and Texturing of 3D Surfaces From Fused Low-Cost Aerial Lidar and Optical Imagery
Authors:
Kiguthi, Samuel
Source:
All Graduate Theses and Dissertations, Fall 2023 to Present
Publisher Information:
DigitalCommons@USU
Publication Year:
2025
Collection:
Utah State University: DigitalCommons@USU
Subject Terms:
LiDAR-camera fusion, Aerial LiDAR, Clustering, Surface Reconstruction, Computer Vision, Delaunay Triangulation, Ball-Pivoting Algorithm, Textured Digital Surface Model, Electrical and Computer Engineering, Engineering
Document Type:
Fachzeitschrift text
File Description:
application/pdf
Language:
unknown
Relation:
https://digitalcommons.usu.edu/etd2023/661; https://digitalcommons.usu.edu/context/etd2023/article/1676/viewcontent/ECEetd2025Dec_Kiguthi_Samuel.pdf
DOI:
10.26076/2760-8e31
Availability:
https://digitalcommons.usu.edu/etd2023/661
https://doi.org/10.26076/2760-8e31
https://digitalcommons.usu.edu/context/etd2023/article/1676/viewcontent/ECEetd2025Dec_Kiguthi_Samuel.pdf
Rights:
Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact digitalcommons@usu.edu.
Accession Number:
edsbas.FDB05F82
Database:
BASE

Weitere Informationen

Drones equipped with laser scanners (LiDAR) and cameras capture detailed 3D scenes. Combining the laser points with photos builds realistic, photo-textured 3D maps used in surveying, agriculture, and infrastructure planning. Conventional methods to create these maps often misrepresent inward shapes such as doorways, overhangs, and outcrops. These shapes are misrepresented because drones mainly view top surfaces and do not collect significant data on vertical or hidden areas. This study introduces a surface-reconstruction method that groups LiDAR points into clusters, reconstructs smooth surfaces for each cluster, and uses information from recorded camera poses to stitch clusters together. Tests on real drone flights with synchronized LiDAR, imagery, and navigation data show that the method better preserves concave features, mitigates errors from sparse sampling, and improves the completeness and accuracy of the resulting 3D maps.