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• W3080713753 endingPage "4819" @default.
• W3080713753 startingPage "4819" @default.
• W3080713753 abstract "Flat surfaces captured by 3D point clouds are often used for localization, mapping, and modeling. Dense point cloud processing has high computation and memory costs making low-dimensional representations of flat surfaces such as polygons desirable. We present Polylidar3D, a non-convex polygon extraction algorithm which takes as input unorganized 3D point clouds (e.g., LiDAR data), organized point clouds (e.g., range images), or user-provided meshes. Non-convex polygons represent flat surfaces in an environment with interior cutouts representing obstacles or holes. The Polylidar3D front-end transforms input data into a half-edge triangular mesh. This representation provides a common level of abstraction for subsequent back-end processing. The Polylidar3D back-end is composed of four core algorithms: mesh smoothing, dominant plane normal estimation, planar segment extraction, and finally polygon extraction. Polylidar3D is shown to be quite fast, making use of CPU multi-threading and GPU acceleration when available. We demonstrate Polylidar3D’s versatility and speed with real-world datasets including aerial LiDAR point clouds for rooftop mapping, autonomous driving LiDAR point clouds for road surface detection, and RGBD cameras for indoor floor/wall detection. We also evaluate Polylidar3D on a challenging planar segmentation benchmark dataset. Results consistently show excellent speed and accuracy." @default.
• W3080713753 created "2020-09-01" @default.
• W3080713753 creator A5000130413 @default.
• W3080713753 creator A5039640897 @default.
• W3080713753 date "2020-08-26" @default.
• W3080713753 modified "2023-09-25" @default.
• W3080713753 title "Polylidar3D-Fast Polygon Extraction from 3D Data" @default.
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• W3080713753 doi "https://doi.org/10.3390/s20174819" @default.
• W3080713753 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7506964" @default.
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