Hybrid simplification algorithm for unorganized point cloud based on two-level fuzzy decision making

Zhang, Chaolong; Zhou, Haibo; Chen, Boyu; Peng, Yichang; Duan, Jinhui

doi:10.1016/j.ijleo.2023.170642

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Along with standard features such as normal and curvature, it also contains point cloud subsampling that uses characteristics such as edge form and density. Zhang et al [38] proposed a hybrid point cloud subsampling method based on twolevel fuzzy decision making by counting point cloud density histograms, while other methods achieve feature-preserving point cloud subsampling by detecting shape features such as edges and feature lines of the point cloud [39]. Some work introduces entropy to assess the feature importance of points [12,40].…”

Section: Point Cloud Subsamplingmentioning

confidence: 99%

Fast subsampling strategy for point cloud based on novel octree coding

Zheng,

Wang,

Zha

et al. 2024

Meas. Sci. Technol.

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Owing to the continuous expansion in data scale, the calculation, storage, and transmission of 3D data have been plagued by numerous issues. The point cloud data, in particular, often contain duplicated and anomalous points, which can hinder tasks such as measurement. To address this issue, it is crucial to utilize point cloud preprocessing methods that combine subsampling and denoising. These methods help obtain clean, evenly distributed, and compact points to enhance the accuracy of the data. In this study, an efficient point cloud subsampling method is proposed that combines point cloud denoising capabilities. This method can effectively preserve salient features while improving the quality of point cloud data. By constructing the octree structure of the point cloud, the corresponding node code is obtained according to the spatial coordinates of the point cloud, and the feature vector of the node is calculated based on the analysis of covariance. Node feature similarity is introduced to distinguish the node into feature and non-feature nodes, forming the node feature code, and the layer threshold is introduced to filter outliers. Experimental results showed that this approach is effective in removing noise while preserving important features, thereby reducing the complexity of the point cloud. The algorithm can serve as a reference for fast and precise measurement that demands high timeliness owing to its high computational efficiency.

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