Journal Article•
Feature preserving point cloud simplification
TL;DR: The proposed feature preserving point cloud simplification method obtains the smallest simplification error and preserves original geometric features well and generates sparse sampling points in flat areas and high density points in high curvature regions.
Abstract: 3Dscanning devices generally produce a large amount of dense points.This paper presents a feature preserving point cloud simplification method to reduce redundant points while preserving original geometric features well.Firstly,K-mean clustering algorithm was employed to globally gather similar points in a spatial domain.By constructing aK-dtree structure for the point cloud,some nodes of the K-d tree were used as initial clustering centroids.Then,normal vector of point cloud and candidate feature points were estimated with principal component analysis method.Traversing every cluster,if feature points were contained in the cluster,the cluster was subdivided into a series of sub-clusters and the cluster was mapped to a Gaussian sphere.Finally,adaptive mean shift algorithm was employed to classify the data in Gaussian sphere and the clusters in Gaussian sphere were corresponded to the sub-clusters in the spatial domain.The cluster centroids present the simplification data.Several real object models were used to verify the effectiveness of the proposed method.The experiment results demonstrate that the proposed method generates sparse sampling points in flat areas and high density points in high curvature regions.As comparing with the nonuniform grid,hierarchical agglomerative,and K-means methods,the proposed method obtains thesmallest simplification error and preserves original geometric features.
Citations
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TL;DR: A new method for point cloud simplification, named FPPS (feature-preserved point clouds simplification), by which the simplification rules are set and the results show that FPPS is superior to other simplification algorithms.
Abstract: With the development of 3D scanning technology, a huge volume of point cloud data has been collected at a lower cost. The huge data set is the main burden during the data processing of point clouds, so point cloud simplification is critical. The main aim of point cloud simplification is to reduce data volume while preserving the data features. Therefore, this paper provides a new method for point cloud simplification, named FPPS (feature-preserved point cloud simplification). In FPPS, point cloud simplification entropy is defined, which quantifies features hidden in point clouds. According to simplification entropy, the key points including the majority of the geometric features are selected. Then, based on the natural quadric shape, we introduce a point cloud matching model (PCMM), by which the simplification rules are set. Additionally, the similarity between PCMM and the neighbors of the key points is measured by the shape operator. This represents the criteria for the adaptive simplification parameters in FPPS. Finally, the experiment verifies the feasibility of FPPS and compares FPPS with other four-point cloud simplification algorithms. The results show that FPPS is superior to other simplification algorithms. In addition, FPPS can partially recognize noise.
26 citations
TL;DR: Compared to traditional curvature simplification algorithms, the proposed method has the lowest deviation and highest accuracy at the same simplicity level, as numerous feature points are preserved, which facilitates the point cloud processing.
Abstract: Considering the diversity of point cloud features, the simplification effects of traditional point cloud simplification algorithms (such as curvature, random and isometry simplification algorithms) are poor. To overcome these drawbacks, a point cloud simplification method based on adaptive curvature entropy is proposed. Points with large curvatures are extracted to construct the initial point cloud boundary by defining a given proportion. The point cloud is clustered using the dichotomy clustering method. Subsequently, a preliminary simplification based on an adaptive random algorithm is performed for each clustered point cloud to reduce the point cloud capacity. The curvature entropy of each clustered point cloud is calculated to remove redundant points and preserve feature points so that the simplified point cloud is eventually obtained. The extracted initial point cloud boundary and simplified point cloud constitute the final simplified result. The classic Stanford rabbit model is introduced to verify the effect of the proposed approach. Experimental results show that the proposed algorithm can effectively reflect the details of the point cloud despite a simplification proportion of up to 90%. Compared to traditional curvature simplification algorithms, the proposed method has the lowest deviation and highest accuracy at the same simplicity level, as numerous feature points are preserved, which facilitates the point cloud processing.
14 citations
TL;DR: The experimental results show that the proposed feature line extraction method can accurately extract the feature points, with good noise immunity, especially suitable for the massive point cloud model.
Abstract: In order to improve the accuracy and rapidity of feature line extraction from point clouds, the work proposed a feature line extraction method based on geometric structure of point space. Firstly, a spatial grid dynamic division method is designed to locate the feature region of the model. A new feature points detection operator based on the linear intercept ratio is proposed according to the geometric information of points. Then, the feature points are refined by the Laplacian operator. Finally, the refined feature points are connected into the characteristic curve by the improved method of polyline growth. Compared with the feature points detection method based on surface variation (MSSV) or the angle of normal vector (SM-PD), the proposed method has low rate of error recognition with the increased noise intensity. Meanwhile, the computation time is 224.42 ms for the standard Armadillo model, less than 530.23 ms of the MSSV and 350.75 ms of the SM-PD. The experimental results show that the proposed method can accurately extract the feature points, with good noise immunity, especially suitable for the massive point cloud model.
12 citations
TL;DR: In this paper , the authors proposed a method for point cloud simplification based on the concept of partitioning, which divides the point cloud's points into edge points, feature points, and non-feature points.
5 citations
TL;DR: The method of extracting feature points by multi-discriminant parameters is used to simplify the point cloud and shows that the combination of feature point extraction and hole repair not only improves the efficiency of the repair process, but also retains the original features of the data.
Abstract: To address the problem of feature points missing and surface holes after 3D reconstruction. A method of filling holes after extracting feature points of point cloud data is proposed. In this paper, the method of extracting feature points by multi-discriminant parameters is used to simplify the point cloud. The original shape features are retained while the data are simplified. Then, the initial triangulation of the point cloud is carried out. The resulting triangular mesh is least squared to get a relatively normal mesh, and finally the curvature of the surface is adjusted and optimized. The experimental results show that the simplification rate corresponding to improving the efficiency and retaining the original features is different for the point cloud data of different magnitude. The simplification rate of tens of thousands point cloud data is 70-80%, and that of hundreds of thousands of point cloud data is 40%. It also shows that the combination of feature point extraction and hole repair not only improves the efficiency of the repair process, but also retains the original features of the data.
3 citations
References
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TL;DR: A new method for point cloud simplification, named FPPS (feature-preserved point clouds simplification), by which the simplification rules are set and the results show that FPPS is superior to other simplification algorithms.
Abstract: With the development of 3D scanning technology, a huge volume of point cloud data has been collected at a lower cost. The huge data set is the main burden during the data processing of point clouds, so point cloud simplification is critical. The main aim of point cloud simplification is to reduce data volume while preserving the data features. Therefore, this paper provides a new method for point cloud simplification, named FPPS (feature-preserved point cloud simplification). In FPPS, point cloud simplification entropy is defined, which quantifies features hidden in point clouds. According to simplification entropy, the key points including the majority of the geometric features are selected. Then, based on the natural quadric shape, we introduce a point cloud matching model (PCMM), by which the simplification rules are set. Additionally, the similarity between PCMM and the neighbors of the key points is measured by the shape operator. This represents the criteria for the adaptive simplification parameters in FPPS. Finally, the experiment verifies the feasibility of FPPS and compares FPPS with other four-point cloud simplification algorithms. The results show that FPPS is superior to other simplification algorithms. In addition, FPPS can partially recognize noise.
26 citations
TL;DR: Compared to traditional curvature simplification algorithms, the proposed method has the lowest deviation and highest accuracy at the same simplicity level, as numerous feature points are preserved, which facilitates the point cloud processing.
Abstract: Considering the diversity of point cloud features, the simplification effects of traditional point cloud simplification algorithms (such as curvature, random and isometry simplification algorithms) are poor. To overcome these drawbacks, a point cloud simplification method based on adaptive curvature entropy is proposed. Points with large curvatures are extracted to construct the initial point cloud boundary by defining a given proportion. The point cloud is clustered using the dichotomy clustering method. Subsequently, a preliminary simplification based on an adaptive random algorithm is performed for each clustered point cloud to reduce the point cloud capacity. The curvature entropy of each clustered point cloud is calculated to remove redundant points and preserve feature points so that the simplified point cloud is eventually obtained. The extracted initial point cloud boundary and simplified point cloud constitute the final simplified result. The classic Stanford rabbit model is introduced to verify the effect of the proposed approach. Experimental results show that the proposed algorithm can effectively reflect the details of the point cloud despite a simplification proportion of up to 90%. Compared to traditional curvature simplification algorithms, the proposed method has the lowest deviation and highest accuracy at the same simplicity level, as numerous feature points are preserved, which facilitates the point cloud processing.
14 citations
TL;DR: The experimental results show that the proposed feature line extraction method can accurately extract the feature points, with good noise immunity, especially suitable for the massive point cloud model.
Abstract: In order to improve the accuracy and rapidity of feature line extraction from point clouds, the work proposed a feature line extraction method based on geometric structure of point space. Firstly, a spatial grid dynamic division method is designed to locate the feature region of the model. A new feature points detection operator based on the linear intercept ratio is proposed according to the geometric information of points. Then, the feature points are refined by the Laplacian operator. Finally, the refined feature points are connected into the characteristic curve by the improved method of polyline growth. Compared with the feature points detection method based on surface variation (MSSV) or the angle of normal vector (SM-PD), the proposed method has low rate of error recognition with the increased noise intensity. Meanwhile, the computation time is 224.42 ms for the standard Armadillo model, less than 530.23 ms of the MSSV and 350.75 ms of the SM-PD. The experimental results show that the proposed method can accurately extract the feature points, with good noise immunity, especially suitable for the massive point cloud model.
12 citations
TL;DR: The method of extracting feature points by multi-discriminant parameters is used to simplify the point cloud and shows that the combination of feature point extraction and hole repair not only improves the efficiency of the repair process, but also retains the original features of the data.
Abstract: To address the problem of feature points missing and surface holes after 3D reconstruction. A method of filling holes after extracting feature points of point cloud data is proposed. In this paper, the method of extracting feature points by multi-discriminant parameters is used to simplify the point cloud. The original shape features are retained while the data are simplified. Then, the initial triangulation of the point cloud is carried out. The resulting triangular mesh is least squared to get a relatively normal mesh, and finally the curvature of the surface is adjusted and optimized. The experimental results show that the simplification rate corresponding to improving the efficiency and retaining the original features is different for the point cloud data of different magnitude. The simplification rate of tens of thousands point cloud data is 70-80%, and that of hundreds of thousands of point cloud data is 40%. It also shows that the combination of feature point extraction and hole repair not only improves the efficiency of the repair process, but also retains the original features of the data.
3 citations
TL;DR: A method to segment an equipment’s base from the equipment according to the change trend of the horizontally-projected areas of the layers formed by layering the equipment, thereby reducing the workload of manual segmentation of the base and improving the efficiency of intelligent identification of substation equipment.
Abstract: Segmenting affiliated facilities from the point cloud is the key of 3D identification of the equipment. This paper proposes a method to segment an equipment’s base from the equipment according to the change trend of the horizontally-projected areas of the layers formed by layering the equipment, thereby reducing the workload of manual segmentation of the base and improving the efficiency of intelligent identification of substation equipment. At the same time, the paper improves Iterative Closest Point (ICP) algorithm by using ICP error to jump out early of the iteration process of ICP, to reduce the iteration steps and shorten the matching time. The experimental results show that the identification rate of substation equipment is greatly improved by the base segmenting algorithm. The improved ICP algorithm significantly shortens the identification time, and has little impact on the identification rate.
2 citations