We present a hierarchical extraction algorithm to extract pole-like objects (PLOs) from scene point clouds. First, the point clouds are divided into a set of data blocks along the x- and y-axes after computing the dimensionality structure of each point. An effective height constrained voxel-based segmentation algorithm is proposed to segment the scene point clouds. The adjacent voxels with similar heights are grouped into an individual object based on the spatial proximity and height information. The objects consisting of a smaller number of voxels and most of the linear points are extracted and regarded as the pole-like candidates (PLCs). Then a Euclidean distance clustering algorithm is adopted to segment the PLCs and remove the floating and short segments. Next, each PLC is divided along the z-axis to extract the vertical structure. The straightness of the vertical structure is computed to remove the false PLOs. Finally, a collection of characteristics, such as point distribution and size, are applied to classify the PLOs into a street light pole, high-mast light, beacon light, and single pole. The experimental results demonstrate that our method can extract PLOs quickly and effectively.
KEYWORDS: 3D modeling, Clouds, Reconstruction algorithms, Binary data, Optical engineering, Chemical elements, Data modeling, Laser scanners, Systems modeling, 3D scanning
We present a method to reconstruct the three-dimensional (3-D) Tang Dynasty building model from raw point clouds. Different from previous building modeling techniques, our method is developed for the Tang Dynasty building which does not exhibit planar primitives, facades, and repetitive structural elements as residential low- or high-rise buildings. The proposed method utilizes the structural property of the Tang Dynasty building to process the original point clouds. First, the raw point clouds are sliced into many parallel layers to generate a top-bottom hierarchical structure, and each layer is resampled to achieve a subset purification of 3-D point clouds. In addition, a series of different building components of the building are recognized by clustering these purifications of 3-D point clouds. In particular, we get the tree-structured topology of these different building components during slicing and clustering. Second, different solutions are explored to reconstruct its 3-D model for different building components. The overall model of building can be gotten based on the building components and tree-structured topology. Experimental results demonstrate that the proposed method is more efficient for generating a high realistic 3-D model of the Tang Dynasty building.
KEYWORDS: Buildings, Clouds, 3D modeling, Data modeling, Scene classification, Image segmentation, Optical engineering, LIDAR, 3D image reconstruction, 3D image processing
The three-dimensional modeling of urban scenes is an important topic that can be used for various applications. We present a comprehensive strategy to reconstruct a scene from urban point clouds. First, the urban point clouds are classified into the ground points, planar points on the ground, and nonplanar points on the ground by using the support vector machine algorithm which takes several differential geometry properties as features. Second, the planar points and nonplanar points on the ground are segmented into patches by using different segmentation methods. A collection of characteristics of point cloud segments like height, size, topological relationship, and ratio between the width and length are applied to extract different objects after removing the unwanted segments. Finally, the buildings, ground, and trees in the scene are reconstructed, resulting in a hybrid model representing the urban scene. Experimental results demonstrate that the proposed method can be used as a robust way to reconstruct the scene from the massive point clouds.
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