# A Simplification Method for Point Cloud of T-Profile Steel Plate for Shipbuilding
###### Abstract  
According to the requirements of point cloud simplification for T-profile steel plate welding in shipbuilding, the disadvantages of the existing simplification algorithms are analyzed. In this paper, a point cloud simplification method is proposed based on octree coding and the threshold of the surface curvature feature. In this method, the original point cloud data are divided into multiple sub-cubes with specified side lengths by octree coding, and the points that are closest to the gravity center of the sub-cube are kept. The \(k\)-neighborhood method and the curvature calculation are performed in order to obtain the curvature features of the point cloud. Additionally, the point cloud data are divided into several regions based on the given adjustable curvature threshold. Finally, combining the random sampling method with the simplification method based on the regional gravity center, the T-profile point cloud data can be simplified. In this study, after obtaining the point cloud data of a T-profile plate, the proposed simplification method is compared with some other simplification methods. It is found that the proposed simplification method for the point cloud of the T-profile steel plate for shipbuilding is faster than the three existing simplification methods, while retaining more feature points and having approximately the same reduction rates. point cloud data; simplification method; curvature feature; data reduction +
Footnote †: journal:  
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in the literature [14; 15]. As addressed by [PERSON] [10], it is necessary to construct mesh using the original point cloud data when mesh-based simplification is used. Thus, a lot of computational resources are needed by this process of mesh construction. On the other hand, point-based methods have become a focus for research of point cloud simplification. [PERSON] et al. [16] proposed a point cloud data reduction method based on curvature and uniform reduction. In this method, the \(k\)-nearest neighbors are firstly established by the bounding box method, and then the curvature in the point cloud region is determined and classified according to the principle of the curvature reduction method. More points of the areas with larger curvatures are kept. The simplification method proposed in the study of [PERSON] [17] is mainly for the surface reconstruction of the ship's shell plating. In this study, the point cloud data of the flat area is reduced by the bounding box method, and the minimum distance algorithm is used for reducing the point cloud data of the sharp area. Considering the shape of T-profile plates made through welding in the ship plants, [PERSON] and [PERSON] [18] proposed a point cloud simplification method based on the combination of octree coding and the surface curvature feature threshold. This method can preserve the features of the target object and guarantee the processing speed. [PERSON] et al. [19] presented an adaptive simplification method to reduce the scanned dense 3D points, and designed an automatic recursive subdivision scheme to remove redundant 3D points. [PERSON] et al. [20] presented a dataset simplification method for the RGB-D type point cloud in terms of two types of segments, including incremental planar segmentation of a gradually expanding point cloud map and a method for efficient triangulation and texturing of planar surface segments. [PERSON] et al. [21] developed an algorithm that can reduce the point cloud with the precision criteria by 99%, and they can still accurately resolve the geometry of the scanned object after performing this algorithm. [PERSON] et al. [22] proposed a point cloud simplification algorithm with the edge based on the normal vector. Thus, the distinct features of the edge were preserved.