Geometric Properties Estimation from Line Point Clouds Using Gaussian-Weighted Discrete Derivatives

An, Yi; Wang, Lei; Ma, Rui; Wang, Jinyu

doi:10.1109/tie.2020.2965456

Cited by 6 publications

(7 citation statements)

References 24 publications

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“…The estimation of the local curvature of p i is the ideal mathematical variable to analyse the geometric selfsimilarity and minimization of the redundancy of a set of points. Therefore, using the initial estimate of the curvature of a point p i , it is possible to estimate the weighted Gaussian mean of the curvatures around a neighbourhood (Equation 3) (An et al 2021).…”

Section: Extracting Edge Points From the Perceptual Roughness Metricmentioning

confidence: 99%

3D Cartographic Generalization of LiDAR Point Clouds Based on the Principle of Self-Similarity of a Deterministic Fractal Structure

Freiman¹,

Santos

Reis

2023

Anu. Inst. Geociênc.

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The rendering of virtual three-dimensional (3D) structures represented by Point Cloud (PC) allows the representation of internal and/or external environments to buildings. However, the compilation of 3D geometric models is influenced by the intrinsic characteristics of PCs, which can be mitigated by the application of an PC simplification operator. According to the mathematical norms of fractal geometry, it was assumed that a PC is characterized by self-similarity. Two experimental datasets acquired with an SLT in static mode indoors were used. Four tasks were accomplished: sampling and structuring of a PC to solve the problem of random distribution, from an octree structure; estimation of the curvature of the points and the roughness of a neighbourhood for the extraction of edge points by the analysis of self-similarity and application of the Statistical Outliers Remove (SOR) algorithm, for the elimination of outliers points; uniform voxelization, to simplify the intermediate points; application of the Iterative Closest Point (ICP) algorithm to register the sets generated in the same local coordinate system. The use of voxelization was satisfactory, but once the voxel size is manually defined, the PC can be oversimplified and lose essential characteristics. This can be minimized by the primary analysis of the edge points, generating a set that is uniform, less noisy, and self- similar to the original set. To achieve a minimum density of points to model an environment three-dimensionally, one must analyse the geometric self- similarity characteristics of the PC to produce a simplified set self-similar to the original, considering the premises of fractal geometry. It is recommended to create an automatic simplification process to minimize the subjectivity coming from the analyst.

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Section: Extracting Edge Points From the Perceptual Roughness Metricmentioning

confidence: 99%

3D Cartographic Generalization of LiDAR Point Clouds Based on the Principle of Self-Similarity of a Deterministic Fractal Structure

Freiman¹,

Santos

Reis

2023

Anu. Inst. Geociênc.

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“…Through the internal parameter matrix A, the solution point set A in the spherical coordinate system could be mapped to the two-dimensional image coordinate system. The mapping formula is shown in Equations ( 7) and (8).…”

Section: Threshold Value Of Central Anglementioning

confidence: 99%

“…Each element in the point set is a matrix with a size of 3 × 1. The number of point sets is also n. For subsequent calculations, this method normalizes each element X S Img_n in the point set X S Img , and the normalization method is shown in Equation (8).…”

Section: Threshold Value Of Central Anglementioning

confidence: 99%

“…Moreover, the navigation precision of satellite systems deteriorates rapidly when subjected to interference, resulting in errors. In contrast, the performance of an inertia and satellite combined navigation system heavily relies on the precision of the inertial navigation component [7,8]. With highprecision inertial navigation equipment and stable satellite signals, this system facilitates stable aircraft positioning.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Earth’s Central Angle Threshold and Measurement Model Construction Method for Pose and Attitude Solution Based on Aircraft Scene Matching

Liu,

Gong,

Liu

et al. 2023

Applied Sciences

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To address the challenge of solving aircraft’s visual navigation results using scene matching, this paper introduces the spherical EPnP positioning posture-solving method, which incorporates the threshold value for the central angle and the construction of a measurement model. The detailed steps are as follows: Firstly, the positioning coordinate model of the Earth’s surface is constructed to ensure the expression of the three-dimensional coordinates of the Earth’s surface. The positioning is then solved by employing the EPnP positioning posture-solving algorithm on the constructed data model. Secondly, by comparing and analyzing the positioning posture values of approximate plane coordinates, the critical value is determined, which serves as a reference for plane calculations. Lastly, a theoretical measurement model for visual height and central angle is constructed, taking into account the decided central angle threshold value. The simulation experiment demonstrates that using spherical coordinates as input results in an average positioning precision that is 16.42 percent higher compared to using plane coordinates as input. When the central angle is less than 0.5 degrees and the surface area is smaller than 558502 square meters, the positioning precision of plane coordinates is comparable to that of spherical coordinates. In such instances, the sphere can be approximated as flat. The findings of this study provide important theoretical guidance for further research on scene-matching positioning posture solving. These results hold significant implications for both theoretical research and engineering applications.

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“…Traditional methods based on hand-designed features artificially design feature descriptors according to different problems and then use machine learning methods to complete classification and segmentation of point clouds. Many previous studies have proposed a variety of different local feature descriptors for point clouds to handle different problems [25], [26]. Common descriptors of point clouds can be divided into two categories: statistical feature descriptors and geometrical feature descriptors.…”

Section: Related Workmentioning

confidence: 99%

Lightweight Attention Module for Deep Learning on Classification and Segmentation of 3-D Point Clouds

Cui

Sun

et al. 2021

IEEE Trans. Instrum. Meas.

Self Cite

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Research on classification and segmentation of 3-D point clouds using deep learning methods has become a hot topic in emerging applications, such as autonomous driving, augmented reality, and indoor navigation. However, as the complexity of the network structures increases, the computational efficiency reduces, which affects the practical applications of these methods. In addition, prior researchers mostly seek to enhance the quality of spatial encodings, while the channel relationships are ignored. It makes the feature learning of point clouds insufficient, which will reduce the accuracy of classification and segmentation. In this article, a lightweight attention module (LAM) is proposed to improve the computational efficiency and accuracy at the same time by adopting a novel convolution mode and introducing a new attention mechanism based on channelwise statistical features. As the submodules of LAM, the lightweight module and the attention module can also be used independently to focus on improving the computational efficiency and accuracy, respectively, according to the actual applications. LAM and its submodules can be easily integrated into state-of-the-art deep learning methods on classification and segmentation of 3-D point clouds. The experimental results show that the proposed modules have a good performance on benchmark data sets.

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Geometric Properties Estimation from Line Point Clouds Using Gaussian-Weighted Discrete Derivatives

Cited by 6 publications

References 24 publications

3D Cartographic Generalization of LiDAR Point Clouds Based on the Principle of Self-Similarity of a Deterministic Fractal Structure

3D Cartographic Generalization of LiDAR Point Clouds Based on the Principle of Self-Similarity of a Deterministic Fractal Structure

Estimation of Earth’s Central Angle Threshold and Measurement Model Construction Method for Pose and Attitude Solution Based on Aircraft Scene Matching

Lightweight Attention Module for Deep Learning on Classification and Segmentation of 3-D Point Clouds

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