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• W2044432738 abstract "This letter presents a 3-D object matching framework to support information extraction directly from 3-D point clouds. The problem of 3-D object matching is to match a template, represented by a group of 3-D points, to a point cloud scene containing an instance of that object. A locally affine-invariant geometric constraint is proposed to effectively handle affine transformations, occlusions, incompleteness, and scales in 3-D point clouds. The 3-D object matching framework is integrated into 3-D correspondence computation, 3-D object detection, and point cloud object classification in mobile laser scanning (MLS) point clouds. Experimental results obtained using the 3-D point clouds acquired by a RIEGL VMX-450 system showed that completeness, correctness, and quality of over 0.96, 0.94, and 0.91 are achieved, respectively, with the proposed framework in 3-D object detection. Comparative studies demonstrate that the proposed method outperforms the two existing methods for detecting 3-D objects directly from large-volume MLS point clouds." @default.
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• W2044432738 date "2015-03-01" @default.
• W2044432738 modified "2023-10-03" @default.
• W2044432738 title "Three-Dimensional Object Matching in Mobile Laser Scanning Point Clouds" @default.
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• W2044432738 doi "https://doi.org/10.1109/lgrs.2014.2347347" @default.
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