A point cloud registration method combining enhanced particle swarm optimization and iterative closest point method

Ge, Yuqin; Wang, Baoyun; Nie, Jianhui; Sun, Bo

doi:10.1109/ccdc.2016.7531460

Cited by 12 publications

(7 citation statements)

References 12 publications

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“…Third, there are many strategies to find corresponding points according to the similarity of point features between the source point cloud and the target point cloud. Cirujeda et al [14] proposed a registration strategy based on the game theory method, and there are also strategies based on the genetic algorithm (GA) [1] and particle swarm optimization (PSO) [15]. Finally, the transformation matrix between the two-point clouds is obtained through a series of corresponding points.…”

Section: Related Workmentioning

confidence: 99%

Point Cloud Registration Algorithm Based on the Grey Wolf Optimizer

Feng

Tang

et al. 2020

IEEE Access

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In view of the long computation time and low registration accuracy of the current point cloud registration algorithm, a point cloud registration algorithm based on the grey wolf optimizer (GWO) is proposed, denoted PCR-GW. The algorithm uses the centralization method to solve the translation matrix and then simplifies the points of the initial point cloud models by using the intrinsic shape signatures (ISS) feature. Next, various parameters of the rotation matrix are obtained via the GWO algorithm by employing the quadratic sum of the distances between corresponding points in the simplified point cloud as the objective function. Finally, the point cloud registration process is completed by using the obtained transformation matrix. By conducting a registration experiment on the point cloud library model and comparing PCR-GW with the traditional algorithms, the algorithm proposed in this paper is shown to be promising for improving the computation speed and registration accuracy. INDEX TERMS point cloud registration; feature point extraction; grey wolf optimization algorithm

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Section: Related Workmentioning

confidence: 99%

Point Cloud Registration Algorithm Based on the Grey Wolf Optimizer

Feng

Tang

et al. 2020

IEEE Access

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“…Since point cloud coordinates are used in our 3D teeth reconstruction, the 3D registration is more proper. In the literature, there are several works on 3D registration [ 27 , 28 , 29 , 30 , 31 ] utilizing several features in the registration process, including point cloud coordinates representing the 3D shapes of objects [ 32 , 33 , 34 , 35 , 36 , 37 ]. These mentioned works used a variation swarm optimization (PSO) in the location matching between the source and target images.…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimizer with Behavior Considerations and Dimensional Learning in Three-Dimensional Tooth Model Reconstruction

Wongkhuenkaew,

Auephanwiriyakul,

Chaiworawitkul

et al. 2024

Bioengineering

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Three-dimensional registration with the affine transform is one of the most important steps in 3D reconstruction. In this paper, the modified grey wolf optimizer with behavior considerations and dimensional learning (BCDL-GWO) algorithm as a registration method is introduced. To refine the 3D registration result, we incorporate the iterative closet point (ICP). The BCDL-GWO with ICP method is implemented on the scanned commercial orthodontic tooth and regular tooth models. Since this is a registration from multi-views of optical images, the hierarchical structure is implemented. According to the results for both models, the proposed algorithm produces high-quality 3D visualization images with the smallest mean squared error of about 7.2186 and 7.3999 μm2, respectively. Our results are compared with the statistical randomization-based particle swarm optimization (SR-PSO). The results show that the BCDL-GWO with ICP is better than those from the SR-PSO. However, the computational complexities of both methods are similar.

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“…There exist some 3-D medical image registration methods [13,14] that utilize several features in the registration process including pointcloud coordinates representing the 3-D shapes of objects. These coordinates have also been used in the registration process shown in [15][16][17][18][19][20]. All the mentioned research works utilize a variation of the particle swarm optimization (PSO) to find the matching location between the source and target images.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed SR-PSO algorithm is based on a modification of the particle swarm optimization [21] to cope with the premature convergence [22] and to improve exploration and exploitation of the algorithm [23]. After that, the iterative closet point (ICP) method [24,25] is used to refine the resulting registration because the ICP method has been proved in several research works [8,17] that it can help to refine registered results. Finally, the 3-D tooth models are reconstructed.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization

et al. 2021

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The registration between images is a crucial part of the 3-D tooth reconstruction model. In this paper, we introduce a registration method using our proposed statistical randomization-based particle swarm optimization (SR-PSO) algorithm with the iterative closet point (ICP) method to find the optimal affine transform between images. The hierarchical registration is also utilized in this paper since there are several consecutive images involving in the registration. We implemented this algorithm in the scanned commercial regular-tooth and orthodontic-tooth models. The results demonstrated that the final 3-D images provided good visualization to human eyes with the mean-squared error of 7.37 micrometer2 and 7.41 micrometer2 for both models, respectively. From the results compared with the particle swarm optimization (PSO) algorithm with the ICP method, it can be seen that the results from the proposed algorithm are much better than those from the PSO algorithm with the ICP method.

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A point cloud registration method combining enhanced particle swarm optimization and iterative closest point method

Cited by 12 publications

References 12 publications

Point Cloud Registration Algorithm Based on the Grey Wolf Optimizer

Point Cloud Registration Algorithm Based on the Grey Wolf Optimizer

Grey Wolf Optimizer with Behavior Considerations and Dimensional Learning in Three-Dimensional Tooth Model Reconstruction

Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization

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