Reg3DFacePtCd: Registration of 3D Point Clouds Using a Common Set of Landmarks for Alignment of Human Face Images

Bagchi, Parama; Bhattacharjee, Debotosh; Nasipuri, Mita

doi:10.1007/s13218-019-00593-2

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…However, the study designs are either arbitrary with fiducial markers or based on dry skull models 5,15,16 . Although lately different methods for head orientation in 3D space have been proposed, they have either been focused on 3D facial scans or based on high-resolution medical CT images 4,[17][18][19][20] .…”

Section: Reorientation Methodology For Reproducible Head Posture In S...mentioning

confidence: 99%

Reorientation methodology for reproducible head posture in serial cone beam computed tomography images

Mangal

Lee²,

Lee

et al. 2023

Sci Rep

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Low dose and accessibility have increased the application of cone beam computed tomography (CBCT). Often serial images are captured for patients to diagnose and plan treatment in the craniofacial region. However, CBCT images are highly variable and lack harmonious reproduction, especially in the head’s orientation. Though user-defined orientation methods have been suggested, the reproducibility remains controversial. Here, we propose a landmark-free reorientation methodology based on principal component analysis (PCA) for harmonious orientation of serially captured CBCTs. We analyzed three serial CBCT scans collected for 29 individuals who underwent orthognathic surgery. We first defined a region of interest with the proposed protocol by combining 2D rendering and 3D convex hull method, and identified an intermediary arrangement point. PCA identified the y-axis (anterioposterior) followed by the secondary x-axis (transverse). Finally, by defining the perpendicular z-axis, a new global orientation was assigned. The goodness of alignment (Hausdorff distance) showed a marked improvement (> 50%). Furthermore, we clustered cases based on clinical asymmetry and validated that the protocol was unaffected by the severity of the skeletal deformity. Therefore, it could be suggested that integrating the proposed algorithm as the preliminary step in CBCT evaluation will address a fundamental step towards harmonizing the craniofacial imaging records.

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Section: Reorientation Methodology For Reproducible Head Posture In S...mentioning

confidence: 99%

Reorientation methodology for reproducible head posture in serial cone beam computed tomography images

Mangal

Lee²,

Lee

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…A variant of rigid ICP [81] generalizes the Euclidean distances for the color space, whereas a variant of CPD defines the GMM in the color space of a point cloud [52]. Extrinsic features (e.g., tracked marker locations) over multiple frames [82] can also increase the correspondence reliability during the registration. In contrast, pointbased [19] or geometric features [83] extracted from the input can help to boost the correspondence search.…”

Section: G Defining Boundary Conditionsmentioning

confidence: 99%

Fast Gravitational Approach for Rigid Point Set Registration With Ordinary Differential Equations

et al. 2021

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This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA). In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field. The optimal alignment is obtained by explicit modeling of forces acting on the particles as well as their velocities and displacements with second-order ordinary differential equations of n-body motion. Additional alignment cues can be integrated into FGA through particle masses. We propose a smooth-particle mass function for point mass initialization, which improves robustness to noise and structural discontinuities. To avoid the quadratic complexity of all-to-all point interactions, we adapt a Barnes-Hut tree for accelerated force computation and achieve quasilinear complexity. We show that the new method class has characteristics not found in previous alignment methods such as efficient handling of partial overlaps, inhomogeneous sampling densities, and coping with large point clouds with reduced runtime compared to the state of the art. Experiments show that our method performs on par with or outperforms all compared competing deep-learning-based and general-purpose techniques (which do not take training data) in resolving transformations for LiDAR data and gains state-of-the-art accuracy and speed when coping with different data.

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“…A variant of rigid ICP [89] generalizes the Euclidean distances for the color space, whereas a variant of CPD defines the GMM in the color space of a point cloud [51]. Extrinsic features (e.g., tracked marker locations) over multiple frames [90] can also increase the correspondence reliability during the registration. In contrast, pointbased [19] or geometric features [91] extracted from the input can help to boost the correspondence search.…”

Section: Defining Boundary Conditionsmentioning

confidence: 99%

NRGA: Gravitational Approach for Non-rigid Point Set Registration

Ali

Golyanik

Stricker

2018

2018 International Conference on 3D Vision (3DV)

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This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA). In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field. The optimal alignment is obtained by explicit modeling of forces acting on the particles as well as their velocities and displacements with second-order ordinary differential equations of motion. Additional alignment cues (point-based or geometric features, and other boundary conditions) can be integrated into FGA through particle masses. We propose a smooth-particle mass function for point mass initialization, which improves robustness to noise and structural discontinuities. To avoid prohibitive quadratic complexity of all-to-all point interactions, we adapt a Barnes-Hut tree for accelerated force computation and achieve quasilinear computational complexity. We show that the new method class has characteristics not found in previous alignment methods such as efficient handling of partial overlaps, inhomogeneous point sampling densities, and coping with large point clouds with reduced runtime compared to the state of the art. Experiments show that our method performs on par with or outperforms all compared competing non-deep-learning-based and general-purpose techniques (which do not assume the availability of training data and a scene prior) in resolving transformations for LiDAR data and gains state-of-the-art accuracy and speed when coping with different types of data disturbances.

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Reg3DFacePtCd: Registration of 3D Point Clouds Using a Common Set of Landmarks for Alignment of Human Face Images

Cited by 8 publications

References 30 publications

Reorientation methodology for reproducible head posture in serial cone beam computed tomography images

Reorientation methodology for reproducible head posture in serial cone beam computed tomography images

Fast Gravitational Approach for Rigid Point Set Registration With Ordinary Differential Equations

NRGA: Gravitational Approach for Non-rigid Point Set Registration

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