Registration of 3D point clouds using a local descriptor based on grid point normal

Wang, Jiang; Wu, Bin; Kang, Jiehu

doi:10.1364/ao.437477

Cited by 11 publications

(6 citation statements)

References 31 publications

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“…Therefore, Hao et al [26] proposed an improved weighted covariance matrix to establish a stable and reliable LRF, and comprehensively described the 3D local surface by statistically analyzing multiple geometric distribution features, including voxel density, voxel centroid, and projection density. Furthermore, Wang et al proposed the Grid Normals Deviation Angles Statistics (GNDAS) [27] descriptor, which divides the local surface along the x-axis and yaxis of the local reference coordinate system into grids, and then statistics the deviation angles of the normal vectors at the grid points.…”

Section: Related Workmentioning

confidence: 99%

A novel HPNVD descriptor for 3D local surface description

Sa,

Zhang,

Zhang

et al. 2024

Preprint

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Existing methods for 3D local feature description often struggle to achieve a good balance in distinctiveness, robustness, and computational efficiency. To address this challenge, a novel 3D local feature descriptor named Histograms of Projected Normal Vector Distribution (HPNVD) is proposed. The HPNVD descriptor consists of two main components. First, a Local Reference Frame (LRF) is constructed based on the covariance matrix and neighborhood projection to achieve invariance to rigid transformations. Then, the local surface normals are projected onto three coordinate planes within the LRF, which allows for effective encoding of the local shape information. The projection plane is further divided into multiple regions, and a histogram is computed for each plane to generate the final HPNVD descriptor. Experimental results demonstrate that the proposed HPNVD descriptor achieves a good balance among distinctiveness, robustness, and computational efficiency. Moreover, the HPNVD-based point cloud registration algorithm shows excellent performance, further validating the effectiveness of the descriptor.

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Section: Related Workmentioning

confidence: 99%

A novel HPNVD descriptor for 3D local surface description

Sa,

Zhang,

Zhang

et al. 2024

Preprint

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“…Te use of point cloud normal can provide a better initial position for the matching point cloud and improve the accuracy and efciency of the registration [48][49][50]. In this paper, the kd-tree algorithm is used to achieve fast retrieval of the nearest neighbors [51,52].…”

Section: Estimation Of the Normal Vector Of The Target Planementioning

confidence: 99%

Fast Extraction Algorithm of Planar Targets Based on Point Cloud Data for Monitoring the Synchronization of Bridge Jacking Displacements

Liang,

Zhang,

Zhang

et al. 2024

Structural Control and Health Monitoring

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Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking.

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“…Equation (15) shows that a ij is the linear coefficient of vector Xi with respect to the X j vector and its value reflects the correlation between Xi and X j and between Y i and X j . Thus, the correlation coefficient matrix A reflects the degree to which each of the vectors in χ relates to each of the vectors in γ.…”

Section: Iterative Optimization Based On Linear Expressionmentioning

confidence: 99%

“…The second category comprises point cloud registration algorithms based on local feature matching 13 – 16 To solve the dependence of an iterative algorithm on the initial value, researchers have proposed many feature-based matching algorithms 17 – 21 that are usually divided into two steps: coarse registration and refinement. In the coarse registration stage, the feature points are first extracted from the point cloud data and form the feature description vector.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional point cloud registration algorithm based on the correlation coefficient

Zou

2023

J. Electron. Imag.

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.Point cloud registration is an important research problem in machine vision. One of the most widely used methods for point set registration is the iterative closest point (ICP) algorithm. However, ICP is known to be susceptible to local minima because it is based on the local iterative optimization technique and its performance critically relies on the quality of the initialization. To reduce the possibility of the algorithm falling into local minima, we propose an iterative algorithm based on geometric distance and local feature weighting named weighted correlation coefficient iterative closest point algorithm (WCC_ICP). The algorithm first establishes the corresponding relationship of points using linear representation and constructs an optimization model with constraints. Subsequently, the constrained optimization model is converted into an unconstrained optimization model by Lagrangian multiplication. Finally, an iterative technique is used to solve the optimal rigid body transformation of the optimized model. In addition, in each iteration, the identification ability of the local feature operator of points is used to repair the corresponding relation of points, which reduces the risk of the iterative algorithm falling into the local minima. Numerical experiments show that the proposed WCC_ICP algorithm reduces the risk of the iterative algorithm falling into local minima.

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Registration of 3D point clouds using a local descriptor based on grid point normal

Cited by 11 publications

References 31 publications

A novel HPNVD descriptor for 3D local surface description

A novel HPNVD descriptor for 3D local surface description

Fast Extraction Algorithm of Planar Targets Based on Point Cloud Data for Monitoring the Synchronization of Bridge Jacking Displacements

Three-dimensional point cloud registration algorithm based on the correlation coefficient

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