Point cloud local neighborhood features - a review

Martin Boušek, Jakub Kučera, Hana Váchová

Point cloud local neighborhood features - a review

Číslo: 1/2025
Periodikum: Civil Engineering Journal
DOI: 10.14311/CEJ.2025.01.0005

Klíčová slova: Classification, Filtering, Neighbourhood, Point Cloud, Geometric features

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Anotace: Point clouds are essential for 3D spatial analysis and widely used in geodesy, photogrammetry, and remote sensing. While modern technologies simplify their collection, processing remains challenging due to data size, irregularity, and noise. Classification is critical for object identification and noise removal.

This paper explores geometric features of points derived from their local 3D neighbourhoods. It examines neighbourhood definitions, feature computation via principal component analysis (PCA), and their impact on real dataset classification. Using a test point cloud with natural and anthropogenic features, we analyze feature dependencies, identify redundancies, and highlight key metrics. Additionally, we propose new approaches for noise filtering, contributing to more efficient point cloud processing and practical applications.