Point cloud local neighborhood features - a review
DOI:
https://doi.org/10.14311/CEJ.2025.01.0005Keywords:
Classification, Filtering, Neighbourhood, Point Cloud, Geometric featuresAbstract
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.
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QI, Charles R.; SU, Hao; MO, Kaichun and GUIBAS, Leonidas J. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Online. 2017. Available from: https://doi.org/10.48550/arXiv.1612.00593.
ŠTRONER, Martin; URBAN, Rudolf and SUK, Tomáš. Filtering Green Vegetation Out from Colored Point Clouds of Rocky Terrains Based on Various Vegetation Indices: Comparison of Simple Statistical Methods, Support Vector Machine, and Neural Network. Online. Remote Sensing. 2023, vol. 15, No. 13. ISSN 2072-4292. Available from: https://doi.org/10.3390/rs15133254.
ATIK, Muhammed Enesq and DURAN, Zaide. Classification of aerial photogrammetric point cloud using recurrent neural networks. Online. Fresenius environmental bulletin. 2021, vol. 30, No. 04A/2021, p. 4270-4275. ISSN 1018-4619. Available from: https://www.prt-parlar.de/download_list/?c=FEB_2021#.
LIU, Kunbo; LIU, Shuai; TAN, Kai; YIN, Mingbo and TAO, Pengjie. ANN-Based Filtering of Drone LiDAR in Coastal Salt Marshes Using Spatial–Spectral Features. Online. Remote Sensing. 2024, vol. 16, No. 18. ISSN 2072-4292. Available from: https://doi.org/10.3390/rs16183373.
CHEHATA, Nesrine; GUO, Li and MALLET, Clément. Airborne LIDAR feature selection for urban classification using random forests. Online. Laser scanning, IAPRS. 2009, Vol. XXXVIII. Available from: https://www.researchgate.net/publication/242731335_Airborne_LIDAR_feature_selection_for_urban_classification_using_random_forests.
WEINMANN, M.; JUTZI, B. and MALLET, C. Feature relevance assessment for the semantic interpretation of 3D point cloud data. Online. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2013, vol. II-5/W2, p. 313-318. ISSN 2194-9050. Available from: https://doi.org/10.5194/isprsannals-II-5-W2-313-2013.
WEINMANN, M.; JUTZI, B.; MALLET, C. and WEINMANN, M. GEOMETRIC FEATURES AND THEIR RELEVANCE FOR 3D POINT CLOUD CLASSIFICATION. Online. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017, vol. IV-1/W1, p. 157-164. ISSN 2194-9050. Available from: https://doi.org/10.5194/isprs-annals-IV-1-W1-157-2017. [cit. 2024-11-23].
WEINMANN, Martin; JUTZI, Boris; HINZ, Stefan and MALLET, Clément. Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers. Online. ISPRS Journal of Photogrammetry and Remote Sensing. 2015, vol. 105, p. 286-304. ISSN 09242716. Available from: https://doi.org/10.1016/j.isprsjprs.2015.01.016.
JOLLIFFE, Ian T. and CADIMA, Jorge. Principal component analysis: a review and recent developments. Online. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2016, vol. 374, No. 2065. ISSN 1364-503X. Available from: https://doi.org/10.1098/rsta.2015.0202.
BARNETT, T. P. and PREISENDORFER, R. Origins and Levels of Monthly and Seasonal Forecast Skill for United States Surface Air Temperatures Determined by Canonical Correlation Analysis. Monthly Weather Review. 1987, vol. 115, no. 9, p. 1825-1850. ISSN 0027-0644. Available from: https://doi.org/10.1175/1520-0493(1987)115<1825:OALOMA>2.0.CO;2.
HACKEL, Timo; WEGNER, Jan D. and SCHINDLER, Konrad. Contour Detection in Unstructured 3D Point Clouds. Online. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016, p. 1610-1618. ISBN 978-1-4673-8851-1. Available from: https://doi.org/10.1109/CVPR.2016.178.
Compute geometric features. Online. CLOUDCOMPARE. CloudCompare Wiki. 2024, 7. January 2024. Available from: https://www.cloudcompare.org/doc/wiki/index.php?title=Compute_geometric_features.
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