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• W4293211645 endingPage "6210" @default.
• W4293211645 startingPage "6210" @default.
• W4293211645 abstract "Mobile light detection and ranging (LiDAR) sensor point clouds are used in many fields such as road network management, architecture and urban planning, and 3D High Definition (HD) city maps for autonomous vehicles. Semantic segmentation of mobile point clouds is critical for these tasks. In this study, we present a robust and effective deep learning-based point cloud semantic segmentation method. Semantic segmentation is applied to range images produced from point cloud with spherical projection. Irregular 3D mobile point clouds are transformed into regular form by projecting the clouds onto the plane to generate 2D representation of the point cloud. This representation is fed to the proposed network that produces semantic segmentation. The local geometric feature vector is calculated for each point. Optimum parameter experiments were also performed to obtain the best results for semantic segmentation. The proposed technique, called SegUNet3D, is an ensemble approach based on the combination of U-Net and SegNet algorithms. SegUNet3D algorithm has been compared with five different segmentation algorithms on two challenging datasets. SemanticPOSS dataset includes the urban area, whereas RELLIS-3D includes the off-road environment. As a result of the study, it was demonstrated that the proposed approach is superior to other methods in terms of mean Intersection over Union (mIoU) in both datasets. The proposed method was able to improve the mIoU metric by up to 15.9% in the SemanticPOSS dataset and up to 5.4% in the RELLIS-3D dataset." @default.
• W4293211645 created "2022-08-27" @default.
• W4293211645 creator A5013191451 @default.
• W4293211645 creator A5090736705 @default.
• W4293211645 date "2022-08-18" @default.
• W4293211645 modified "2023-09-25" @default.
• W4293211645 title "An Efficient Ensemble Deep Learning Approach for Semantic Point Cloud Segmentation Based on 3D Geometric Features and Range Images" @default.
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• W4293211645 doi "https://doi.org/10.3390/s22166210" @default.
• W4293211645 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36015964" @default.
• W4293211645 hasPublicationYear "2022" @default.
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