An efficient EM-ICP algorithm for non-linear registration of large 3D point sets

Combès, Benoît; Prima, Sylvain

doi:10.1016/j.cviu.2019.102854

Cited by 10 publications

(4 citation statements)

References 52 publications

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“…The goal of point cloud coarse registration is to reduce the rotation and translation dislocation between the reference point cloud and point cloud to be registered, and provide a good initial value for fine registration [17,31]. The goal of fine registration is to obtain the optimal registration parameters and minimize the errors [38].…”

Section: Methodsmentioning

confidence: 99%

“…The ICP algorithm needs to fulfill two basic conditions: (1) There is a common area between the reference point cloud and point cloud to be registered; and (2) The initial relative deviation between the reference point cloud and point cloud to be registered is not large [25,26]. Some scholars have improved the ICP algorithm and representative studies include Wei et al [27], who proposed an ICP algorithm based on point cloud homography and explained the method of establishing homography point pairs; Li and Song [28], who proposed a vector information registration method based on facet triangles in the stereolithography (STL) format file and introduced a dynamic adjustment factor to speed up the iterative convergence; Cheng et al [29] proposed an improved ICP algorithm, which used a point-to-point correspondence instead of point-to-surface correspondence to reduce the calculation cost; Wu et al [30] proposed a novel robust scale ICP algorithm by introducing the maximum correntropy criterion (MCC) as the similarity measure when the point sets had a large number of outliers and noises; Combès and Prima [31] presented an efficient expectation-maximisation (EM)-ICP algorithm for non-linear pairwise registration of large 3D point sets, compared to other methods using the same "EM-ICP" framework, four key modifications leading to an efficient algorithm: (1) truncation of the cost function; (2) symmetrization of the point-to-point correspondences; (3) specification of priors on these correspondences using differential geometry; and (4) efficient encoding of deformations using the reproducing kernel Hilbert space (RKHS) theory and the Fourier analysis.…”

Section: Introductionmentioning

confidence: 99%

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Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example

Yin

Li²,

et al. 2020

Applied Sciences

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Slope deformation monitoring is the prerequisite for disaster risk assessment and engineering control. Terrestrial laser scanning (TLS) is highly applicable to this field. Coarse registration method of point cloud based on scale-invariant feature transform (SIFT) feature points and fine registration method based on the k-dimensional tree (K-D tree) improved iterative closest point (ICP) algorithm were proposed. The results show that they were superior to other algorithms (such as speeded-up robust features (SURF) feature points, Harris feature points, and Levenberg-Marquardt (LM) improved ICP algorithm) when taking the Stanford Bunny as an example, and had high applicability in coarse and fine registration. In order to integrate the advantages of point measurement and surface measurement, an improved point cloud comparison method was proposed and the optimal model parameters were determined through model tests. A case study was conducted on the left side of the K146 + 150 point at S236 Boshan section, Shandong Province, and research results show that from 14 August 2018 and 9 November 2019, the overall deformation of the slope was small with a maximum value of 0.183 m, and the slope will continue to maintain a stable state without special inducing factors such as earthquake, heavy rainfall and artificial excavation.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example

Yin

Li²,

et al. 2020

Applied Sciences

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“…The variances can be embedded in a coarse-to-fine scheme [19], or they can be estimated in the M-step [31,23]. Several variants have been proposed to handle outliers [21], topological constraints [31], symmetrisation of the registration [9], or non rigid deformations [8]. This approach has been recently extended to the registration of multiple point clouds in [42], where a point is modeled as a mixture of Gaussians centered in the nearest neighbor points in each other point clouds.…”

Section: Related Workmentioning

confidence: 99%

Multiview point cloud registration with anisotropic and space-varying localization noise

Fortun¹,

Baudrier²,

Zwettler³

et al. 2022

Preprint

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In this paper, we address the problem of registering multiple point clouds corrupted with high anisotropic localization noise. Our approach follows the widely used framework of Gaussian mixture model (GMM) reconstruction with an expectation-maximization (EM) algorithm. Existing methods are based on an implicit assumption of space-invariant isotropic Gaussian noise. However, this assumption is violated in practice in applications such as single molecule localization microscopy (SMLM). To address this issue, we propose to introduce an explicit localization noise model that decouples shape modeling with the GMM from noise handling. We design a stochastic EM algorithm that considers noise-free data as a latent variable, with closed-form solutions at each EM step. The first advantage of our approach is to handle space-variant and anisotropic Gaussian noise with arbitrary covariances. The second advantage is to leverage the explicit noise model to impose prior knowledge about the noise that may be available from physical sensors. We show on various simulated data that our noise handling strategy improves significantly the robustness to high levels of anisotropic noise. We also demonstrate the performance of our method on real SMLM data.

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“…Tracking a set of points can be formulated as a registration problem where the goal is to align between two point sets representing a 3D shape at two different times. The Iterative Closest Point algorithm (ICP), originally proposed by Besl and McKay [2] for estimating a global linear transformation to align two point sets, has been extended for non-linear registration of large 3D point sets based on a statistical expectation-maximisation (EM) algorithm [6]. However, robustifying this algorithm face to local minima problems occuring during the optimization of cost function remains a challenging task.…”

Section: Point Cloud Trackingmentioning

confidence: 99%

A new geodesic-based feature for characterization of 3D shapes: application to soft tissue organ temporal deformations

Makki¹,

Bohi²,

Ogier³

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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In this paper, we propose a method for characterizing 3D shapes from point clouds and we show a direct application on a study of organ temporal deformations. As an example, we characterize the behavior of a bladder during a forced respiratory motion with a reduced number of 3D surface points: first, a set of equidistant points representing the vertices of quadrilateral mesh for the surface in the first time frame are tracked throughout a long dynamic MRI sequence using a Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework. Second, a novel geometric feature which is invariant to scaling and rotation is proposed for characterizing the temporal organ deformations by employing an Eulerian Partial Differential Equations (PDEs) methodology. We demonstrate the robustness of our feature on both synthetic 3D shapes and realistic dynamic MRI data portraying the bladder deformation during forced respiratory motions. Promising results are obtained, showing that the proposed feature may be useful for several computer vision applications such as medical imaging, aerodynamics and robotics.

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An efficient EM-ICP algorithm for non-linear registration of large 3D point sets

Cited by 10 publications

References 52 publications

Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example

Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example

Multiview point cloud registration with anisotropic and space-varying localization noise

A new geodesic-based feature for characterization of 3D shapes: application to soft tissue organ temporal deformations

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