A novel point cloud simplification method using local conditional information

Cheng, Yu-qi; Li, Wen-long; Jiang, Cheng; Wang, Dongfang; Mao, Jincheng; Wang, Xu

doi:10.1088/1361-6501/ac8ac1

Cited by 18 publications

(9 citation statements)

References 28 publications

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“…Experimentally, the terracotta head data with the number G10-16 are extracted, and operations such as removing noise from point cloud data [11] , in vitro solitary points, and filling holes are performed. The pre-processed terracotta head data are obtained.…”

Section: Approximation Methods For Intercepting Cross-section Linesmentioning

confidence: 99%

A method of extracting outer eye corners of terra cotta warriors based on point cloud data

Cheng¹,

Liu²

2023

Third International Conference on Image Processing and Intelligent Control (IPIC 2023)

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The Terracotta Warriors and Horses of the First Qin Emperor are a highly prized relic in China and the world. Some people believe that the Terracotta Warriors are a realistic representation of the Qin dynasty, while others feel that the Terracotta Warriors are the result of artistic re-creation. Therefore, in order to verify the "realism" of the terracotta warriors and horses, the degree of resemblance between the terracotta warriors and real people was quantified. The representative points of the outer corners of the eyes on the head and face of the terracotta warriors are selected. The method of extracting feature points based on the cross-sectional line of approximation is proposed using point cloud data. The method is based on the approximation method of cross-sectional line extraction, which is more accurate and easier to calculate by converting 3D into 2D. The final experimental results show that the curvature of the outer corner points of the terracotta warriors and the curvature pattern of the real eyes basically match. The method verifies that the facial features of terracotta warriors are highly correlated with those of real people. At the same time, the "realistic" nature of the terracotta warriors is demonstrated.

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Section: Approximation Methods For Intercepting Cross-section Linesmentioning

confidence: 99%

A method of extracting outer eye corners of terra cotta warriors based on point cloud data

Cheng¹,

Liu²

2023

Third International Conference on Image Processing and Intelligent Control (IPIC 2023)

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“…The construction of polygon meshes with this technique takes a lot of time on point cloud surfaces, and it is simple to smooth down the sharp features of the surface [31]. Point-based methods mainly include methods based on spatial partitioning and those based on point feature information, which directly simplify point clouds without generating additional grids [32]. The method based on spatial partitioning simplifies the subspace by partitioning the point cloud space.…”

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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“…The AXE-B scanner acquires the mesh data with an accuracy of 0.02 mm. Following [26], the variation ratio of single ball diameter error V s and variation ratio of ball bar distance error V t are used to quantitatively evaluate the difference of the geometry parameters, where…”

Section: Performance Verification Experimentsmentioning

confidence: 99%

An adaptive anisotropic bilateral filtering method for mesh data in scale space

Su,

Li,

Cheng

et al. 2024

Meas. Sci. Technol.

Self Cite

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Three-dimensional mesh data of parts, such as blades and engine bodies, have been widely used in industrial fields. Due to the different kinds of noise during mesh acquisition and the machining deficiency of parts, the mesh quality tends to be insufficient for subsequent operations. Therefore, mesh denoising is a necessary and critical procedure to improve mesh quality. Existing methods commonly apply geometry features smoothing, which may also create unexpected results, such as volume shrinkage and blurring of sharp edges. This paper proposed an adaptive anisotropic bilateral filtering method for mesh data in scale space. Firstly, the mesh is decomposed into a smooth base with low frequency and a height vector field with high frequency based on scale space theory. The denoising of the vertex spatial field is transformed into the denoising of the height vector field, aiming to only consider high-frequency information. Secondly, the bilateral filter scheme with the anisotropic Gaussian kernel is proposed to denoise the height vector field, removing noise mixed with features. The parameters in the bilateral filter scheme are chosen adaptively by maximizing the designed probability density function. The mean square angular error of the proposed method is less than 0.15rad, which is superior to the general-purpose algorithms, for instance, Laplacian filtering, bilateral mesh filtering and bilateral normal filtering algorithm.

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A novel point cloud simplification method using local conditional information

Cited by 18 publications

References 28 publications

A method of extracting outer eye corners of terra cotta warriors based on point cloud data

A method of extracting outer eye corners of terra cotta warriors based on point cloud data

Fast subsampling strategy for point cloud based on novel octree coding

An adaptive anisotropic bilateral filtering method for mesh data in scale space

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