Rapid assessment of slope deformation in 3D point cloud considering feature-based simplification and deformed area extraction

He, Leping; Zhou, Yan; Hu, Qijun; Xiang, Bo; Xu, Hongke

doi:10.1088/1361-6501/acafff

Cited by 7 publications

(3 citation statements)

References 71 publications

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“…He et al propose a multi-feature preserving point cloud subsampling method based on octree partitioning, which can effectively retain the feature points. He et al [34] proposed a sub-sampling method based on octree partitioning for a multiparameter feature preserving point cloud, which can effectively preserve the feature points.…”

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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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.

View full text Add to dashboard Cite

show abstract

“…Octree structure is a data model. It divides the geometric objects in a 2n×2n×2n 3D by a recursive loop to form a direction graph with root nodes [13]. For a space object of size 2n×2n×2n, it needs to be partitioned n times at most.…”

Section: Octree Voxel Filteringmentioning

confidence: 99%

Point Cloud Reduction Method Based on Curvature Grading and Voxel Filtering

Zhiheng Ding, Renfu Li

2024

jes

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In the context of automated assembly engineering based on reverse engineering in the aerospace field, redundant data will make it impossible to complete 3D model reconstruction efficiently and quickly, and point cloud data must be streamlined. A single simplification method cannot accurately retain the feature information of the scanned point cloud. Aiming at this background, a point cloud simplification method combining curvature grading and octree voxel filtering is introduced. The obtained curvature is divided into different levels by using the properties of logarithmic function, and the strong and weak feature points are distinguished. The weak feature points are filtered by octree voxel, and the filtered results are merged with the strong feature points to obtain the final streamlined results. This method is compared with the random sampling method and the point cloud simplification method combining 3D-SIFT feature extraction and voxel filtering. The experimental results show that this method not only greatly reduces the amount of data, but also preserves the local details of the original point cloud data, so as to achieve the purpose of efficiently compressing point cloud data.

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“…edge features. For example, in [6], edge features are extracted to keep the structure of slope, enabling more effective deformation monitoring. However, compared with 2D images, detecting edges in 3D point clouds is more challenging.…”

Section: Introductionmentioning

confidence: 99%

Edge detection for 3D point clouds via locally max-angular gaps descriptor

Ma,

Zhang,

Chen

et al. 2023

Meas. Sci. Technol.

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Numerous point-cloud-based applications, including surface reconstruction and completion, greatly benefit from the utilization of edge features, which play a crucial role in structuring the target shape. Nevertheless, the current limitations of point cloud edge detection techniques in effectiveness and efficiency have motivated us to develop a more robust 3D edge detection method. In this paper, we introduce a simple yet effective descriptor for evaluating the edge level of 3D point clouds. This descriptor is defined as the variance of the max-angular gaps between the target point and its k nearest neighboring points. Leveraging this descriptor, we further propose a multiscale feature fusion strategy that well detect the edge features for the target point cloud. Experimental results demonstrate the superiority of our method over other comparison methods in terms of effectiveness and stability in preserving point cloud edges. Moreover, we investigate the applications of our method in point cloud simplification and registration to provide additional insights into its potential use in some point-cloud-based downstream tasks.

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Rapid assessment of slope deformation in 3D point cloud considering feature-based simplification and deformed area extraction

Cited by 7 publications

References 71 publications

Fast subsampling strategy for point cloud based on novel octree coding

Fast subsampling strategy for point cloud based on novel octree coding

Point Cloud Reduction Method Based on Curvature Grading and Voxel Filtering

Edge detection for 3D point clouds via locally max-angular gaps descriptor

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