Pairwise coarse registration of point clouds by traversing voxel-based 2-plane bases

Fu, Yongjian; Li, Zongchun; Xiong, Feng; He, Hong; Wang, Wenqi; Deng, Yong

doi:10.1080/01431161.2022.2130725

Cited by 2 publications

(1 citation statement)

References 58 publications

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“…The rapid development of light detection and ranging (LiDAR) technology has demonstrated substantial advantages in achieving an accurate understanding of the threedimensional (3D) world [1][2][3]. The accurate reconstruction of the fine geometric models of buildings, based on terrestrial laser scanning point clouds, has become an urgently needed research hotspot in the fields of mapping, remote sensing, and computer vision [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Feature Line Extraction from Building Façade Point Clouds by Exploring Spatial Topological Relationship

Xiong,

Li,

et al. 2023

Journal of Sensors

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After using a terrestrial laser scanner to acquire building facade point clouds, the extraction of feature lines can simplify the expression of building objects, thereby contributing to the accurate construction of building facade geometric models. To address the problems of missing extraction and low accuracy in existing methods, this study proposes a feature line extraction method for building facade point clouds by exploring the different spatial topological relationship between feature and non-feature points. The method comprises three steps: feature point extraction, feature line generation, and feature line merging and optimization. First, feature points are extracted using the convex hull distance value and relative angle, as defined in this study. Thereafter, feature points are clustered by combining the random sample consensus and region growing algorithms, and the feature lines are obtained by utilizing the iterative weighted least squares method based on the IGGIII weight function. Finally, the feature lines are merged and optimized using an endpoint search method to improve the discontinuity and missing common endpoints of the original feature lines. The experimental results obtained using simulated and measured point cloud data show that this method can accurately extract feature points, and the extracted feature lines obtained from building facade point clouds have better accuracy and completeness, which is more practical than the existing methods and can be used in many applications such as building facade measurement and urban three-dimensional modeling.

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Section: Introductionmentioning

confidence: 99%

Feature Line Extraction from Building Façade Point Clouds by Exploring Spatial Topological Relationship

Xiong,

Li,

et al. 2023

Journal of Sensors

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A Fast Registration Method for Building Point Clouds Obtained by Terrestrial Laser Scanner via 2-D Feature Points

Tao,

Xiao,

Wang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Point cloud registration plays a central role in various applications, such as 3D scene reconstruction, preservation of cultural heritage and deformation monitoring. The point cloud data are usually huge. Processing such huge data is very time-consuming, so a fast and accurate registration method is crucial. However, the existing registration methods still have high computation complexity or low accuracy. To address this issue, we develop a registration method for terrestrial point clouds. The method projects the point clouds onto the horizontal plane. Therefore, our method processes point cloud data in 2D space, leading to high computation efficiency. Then, the 2D feature lines are extracted from the projected point clouds. We calculate the intersection points of the 2D feature lines, which are treated as the 2D feature points. Due to the high accuracy of the 2D feature lines, the 2D feature points also have high accuracy. Thus, our method can get accurate registration results. Afterward, the feature triangles are constructed by using the 2D feature points, and the geometric constraints are utilized to find the corresponding feature triangles for calculating the 2D transformation. This strategy boosts the process of searching for the corresponding 2D feature points. Subsequently, the Z-axis displacement is computed by the cylindrical neighbourhoods. By combining the Z-axis displacement and 2D transformation, the 3D rigid transformation is obtained. Experimental evaluation conducted on two publicly available datasets well demonstrate that the proposed registration method can achieve good computational efficiency and high accuracy. The code will be available at: https://github.com/taowuyong?tab=repositories after publication.

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Pairwise coarse registration of point clouds by traversing voxel-based 2-plane bases

Cited by 2 publications

References 58 publications

Feature Line Extraction from Building Façade Point Clouds by Exploring Spatial Topological Relationship

Feature Line Extraction from Building Façade Point Clouds by Exploring Spatial Topological Relationship

A Fast Registration Method for Building Point Clouds Obtained by Terrestrial Laser Scanner via 2-D Feature Points

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