Intelligent Point Cloud Edge Detection Method Based on Projection Transformation

Zhu, Jigui; Yue, Xiaofeng; Jian-ping, Huang; Huang, Zongwei

doi:10.1155/2021/2706462

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…These improved alpha shapes methods all set different rolling ball radii according to the topological relationship and density of the local point clouds, which reduces the difficulty of setting manual parameters, but there are still some problems, such as excessive sensitivity to point clouds with large density variations and false extraction of internal holes [22,23]. Point cloud data feature-based extraction algorithms include neighbor point direction distribution [24], Minimum Boundary Rectangle (MBR) [25,26], and virtual grid [27][28][29][30]. Among them, the algorithm for extracting the contour of a building roof using neighbor point direction distribution is more applicable to buildings with different point cloud density changes and complex shapes, and it is easier to set the parameters, but it fails to extract the contour line of a concave area, with relatively low extraction efficiency.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Building Roof Contours from Airborne LiDAR Point Clouds Based on Multidirectional Bands

Wang,

Zang,

et al. 2024

Remote Sensing

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Because of the complex structure and different shapes of building contours, the uneven density distribution of airborne LiDAR point clouds, and occlusion, existing building contour extraction algorithms are subject to such problems as poor robustness, difficulty with setting parameters, and low extraction efficiency. To solve these problems, a building contour extraction algorithm based on multidirectional bands was proposed in this study. Firstly, the point clouds were divided into bands with the same width in one direction, the points within each band were vertically projected on the central axis in the band, the two projection points with the farthest distance were determined, and their corresponding original points were regarded as the roof contour points; given that the contour points obtained based on single-direction bands were sparse and discontinuous, different banding directions were selected to repeat the above contour point marking process, and the contour points extracted from the different banding directions were integrated as the initial contour points. Then, the initial contour points were sorted and connected according to the principle of joining the nearest points in the forward direction, and the edges with lengths greater than a given threshold were recognized as long edges, which remained to be further densified. Finally, each long edge was densified by selecting the noninitial contour point closest to the midpoint of the long edge, and the densification process was repeated for the updated long edge. In the end, a building roof contour line with complete details and topological relationships was obtained. In this study, three point cloud datasets of representative building roofs were chosen for experiments. The results show that the proposed algorithm can extract high-quality outer contours from point clouds with various boundary structures, accompanied by strong robustness for point clouds differing in density and density change. Moreover, the proposed algorithm is characterized by easily setting parameters and high efficiency for extracting outer contours. Specific to the experimental data selected for this study, the PoLiS values in the outer contour extraction results were always smaller than 0.2 m, and the RAE values were smaller than 7%. Hence, the proposed algorithm can provide high-precision outer contour information on buildings for applications such as 3D building model reconstruction.

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Section: Introductionmentioning

confidence: 99%

Extraction of Building Roof Contours from Airborne LiDAR Point Clouds Based on Multidirectional Bands

Wang,

Zang,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

Intelligent Point Cloud Edge Detection Method Based on Projection Transformation

Abstract: An edge detection method based on projection transformation is proposed. First, the vertical projection transformation is carried out on the target point cloud. Data X and data Y are normalized to the width and height of the image, respectively. Data Z … Show more

Cited by 1 publication

References 15 publications

Extraction of Building Roof Contours from Airborne LiDAR Point Clouds Based on Multidirectional Bands

Extraction of Building Roof Contours from Airborne LiDAR Point Clouds Based on Multidirectional Bands

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