Segmentation of unbalanced and in-homogeneous point clouds and its application to 3D scanned trees

Segmentation of 3D point clouds is still an open issue in the case of unbalanced and in-homogeneous data-sets. In the application context of the modeling of botanical trees, a fundamental challenge consists in separating the leaves from the wood. Based on deep learning and a class decision process,...

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Veröffentlicht in:The Visual computer Jg. 36; H. 10-12; S. 2419 - 2431
Hauptverfasser: Morel, Jules, Bac, Alexandra, Kanai, Takashi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2020
Springer Nature B.V
Springer Verlag
Schlagworte:
Artificial Intelligence
Computational Geometry
Computer Graphics
Computer Science
Datasets
Deep learning
Forestry
Image Processing and Computer Vision
Image segmentation
Lasers
Leaves
Methods
Original Article
Remote sensing
Sensors
Three dimensional models
Trees
Deep learning
Point cloud segmentation
Terrestrial LiDAR
Unbalanced data-set
ISSN:0178-2789, 1432-2315, 1432-8726
Online-Zugang:Volltext
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Zusammenfassung:Segmentation of 3D point clouds is still an open issue in the case of unbalanced and in-homogeneous data-sets. In the application context of the modeling of botanical trees, a fundamental challenge consists in separating the leaves from the wood. Based on deep learning and a class decision process, we propose an innovative method designed to separate leaf points from wood points in terrestrial LiDAR point clouds of trees. Although simple, our approach learns trees characteristic point patterns efficiently and robustly. To train our 3D deep learning model, we constructed a 3D labeled point cloud data-set of different tree species. Experiments show that our 3D deep representation together with our geometric approach leads to significant improvement over the state-of-the-art methods in segmentation task.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:0178-2789
1432-2315
1432-8726
DOI:10.1007/s00371-020-01966-7