ArticlePloS one2023
Airborne LiDAR point cloud classification using PointNet++ network with full neighborhood features.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- DK-EffiPointMLP: An Efficient 3D Dorsal Point Cloud Network for Individual Identification of Pigs.Animals : an open access journal from MDPI · 2026Article
- Density-equalized RANDT scan matching with integrated outlier removal and point density uniformity.PloS one · 2026Article
- Toward transparent intelligence: Explainable stacked ensembles learning for LiDAR point cloud segmentation.PloS one · 2026Article
- LiDAR Technology for UAV Detection: From Fundamentals and Operational Principles to Advanced Detection and Classification Techniques.Sensors (Basel, Switzerland) · 2025Review
- Segmentation of LiDAR point cloud data in urban areas using adaptive neighborhood selection technique.PloS one · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Compared with other point clouds, the airborne LiDAR point cloud has its own characteristics. The deep learning network PointNet++ ignores the inherent properties of airborne LiDAR point, and the classification precision is low. Therefore, we propose a framework based on the PointNet++ network. In this work, we proposed an interpolation method that uses adaptive elevation weight to make full use of the objects in the airborne LiDAR point, which exhibits discrepancies in elevation distributions. The class-balanced loss function is used for the uneven density distribution of point cloud data. Moreover, the relationship between a point and its neighbours is captured, densely connecting point pairs in multiscale regions and adding centroid features to learn contextual information. Experiments are conducted on the Vaihingen 3D semantic labelling benchmark dataset and GML(B) benchmark dataset. The experiments show that the proposed method, which has additional contextual information and makes full use of the airborne LiDAR point cloud properties to support classification, achieves high accuracy and can be widely used in airborne LiDAR point classification.
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Registered trials
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