Instance-Aware Semantic Segmentation of Road Furniture in Mobile Laser Scanning Data

In this paper, we present an improved framework for the instance-aware semantic segmentation of road furniture in mobile laser scanning data. In our framework, we first detect road furniture from mobile laser scanning point clouds. Then we decompose the detected pieces of road furniture into poles a...

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Veröffentlicht in:	IEEE transactions on intelligent transportation systems Jg. 23; H. 10; S. 17516 - 17529
Hauptverfasser:	Li, Fashuai, Zhou, Zhize, Xiao, Jianhua, Chen, Ruizhi, Lehtomaki, Matti, Elberink, Sander Oude, Vosselman, George, Hyyppa, Juha, Chen, Yuwei, Kukko, Antero
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Classification Classifiers Datasets Densely connected conditional random fields Feature extraction Furniture instance-aware semantic segmentation Laser applications Lasers Machine learning mobile laser scanning point clouds Point cloud compression pole-like road furniture Roads Scanning Semantic segmentation Semantics Shape Three-dimensional displays
ISSN:	1524-9050, 1558-0016
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Abstract	In this paper, we present an improved framework for the instance-aware semantic segmentation of road furniture in mobile laser scanning data. In our framework, we first detect road furniture from mobile laser scanning point clouds. Then we decompose the detected pieces of road furniture into poles and their attached components, and extract the instance information of the components with different features. Most importantly, we classify the components into different categories by combining a classifier and a probabilistic graphic model named DenseCRF, which is the major contribution of this paper. For the classification of the components using DenseCRF, the unary potentials and the pairwise potentials are first obtained. The unary potentials are obtained from the classifier which takes the instance information of components as the input. The pairwise potentials are calculated considering contextual relations between components. By utilising DenseCRF, the contextual consistency of components is preserved, and the performance is significantly improved compared to our previous work. We collect three datasets to test our framework, and compare the classification performances of six different classifiers with and without DenseCRF. The combination of random forest with DenseCRF outperforms the other methods and achieves high overall accuracies of 83.7%, 96.4% and 95.3% in these three datasets. Experimental results demonstrate that our framework reliably assigns both semantic information and instance information for mobile laser scanning point clouds of road furniture.
AbstractList	In this paper, we present an improved framework for the instance-aware semantic segmentation of road furniture in mobile laser scanning data. In our framework, we first detect road furniture from mobile laser scanning point clouds. Then we decompose the detected pieces of road furniture into poles and their attached components, and extract the instance information of the components with different features. Most importantly, we classify the components into different categories by combining a classifier and a probabilistic graphic model named DenseCRF, which is the major contribution of this paper. For the classification of the components using DenseCRF, the unary potentials and the pairwise potentials are first obtained. The unary potentials are obtained from the classifier which takes the instance information of components as the input. The pairwise potentials are calculated considering contextual relations between components. By utilising DenseCRF, the contextual consistency of components is preserved, and the performance is significantly improved compared to our previous work. We collect three datasets to test our framework, and compare the classification performances of six different classifiers with and without DenseCRF. The combination of random forest with DenseCRF outperforms the other methods and achieves high overall accuracies of 83.7%, 96.4% and 95.3% in these three datasets. Experimental results demonstrate that our framework reliably assigns both semantic information and instance information for mobile laser scanning point clouds of road furniture.
Author	Hyyppa, Juha Li, Fashuai Chen, Yuwei Chen, Ruizhi Lehtomaki, Matti Xiao, Jianhua Elberink, Sander Oude Kukko, Antero Zhou, Zhize Vosselman, George
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SubjectTerms	Classification Classifiers Datasets Densely connected conditional random fields Feature extraction Furniture instance-aware semantic segmentation Laser applications Lasers Machine learning mobile laser scanning point clouds Point cloud compression pole-like road furniture Roads Scanning Semantic segmentation Semantics Shape Three-dimensional displays
Title	Instance-Aware Semantic Segmentation of Road Furniture in Mobile Laser Scanning Data
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