A refined coherent point drift (CPD) algorithm for point set registration

Wang, Peng; Wang, Ping; Qu, Zhiguo; Gao, Yinghui; Zhen-kang, Shen

doi:10.1007/s11432-011-4465-7

Cited by 42 publications

(24 citation statements)

References 11 publications

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“…Such motion regularisation is related to motion coherence, and inspired the algorithm's name. The CPD method was has been extended by various groups [25,26,27,28]. Compared to TPS-RPM, CPD offers superior accuracy and stability with respect to non-rigid deformations in presence of outliers.…”

Section: Dense Shape Registrationmentioning

confidence: 99%

Non-rigid 3D Shape Registration Using an Adaptive Template

Dai

Pears

Smith

2019

Lecture Notes in Computer Science

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We present a new fully-automatic non-rigid 3D shape registration (morphing) framework comprising (1) a new 3D landmarking and pose normalisation method; (2) an adaptive shape template method to improve the convergence of registration algorithms and achieve a better final shape correspondence and (3) a new iterative registration method that combines Iterative Closest Points with Coherent Point Drift (CPD) to achieve a more stable and accurate correspondence establishment than standard CPD. We call this new morphing approach Iterative Coherent Point Drift (ICPD). Our proposed framework is evaluated qualitatively and quantitatively on three datasets: Headspace, BU3D and a synthetic LSFM dataset, and is compared with several other methods. The proposed framework is shown to give state-of-the-art performance.

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Section: Dense Shape Registrationmentioning

confidence: 99%

Non-rigid 3D Shape Registration Using an Adaptive Template

Dai

Pears

Smith

2019

Lecture Notes in Computer Science

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“…Myronenko et al consider the alignment of two point sets as a probability density estimation [15] and they call the method Coherent Point Drift (CPD). The CPD approach is extended in [22], [23], [24], [25]. Dai et al proposed a hierarchical parts-based CPD-LB morphing framework to avoid underfitting and over-fitting [14].…”

Section: Related Workmentioning

confidence: 99%

A Data-Augmented 3D Morphable Model of the Ear

Dai

Pears

Smith

2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

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Abstract-Morphable models are useful shape priors for biometric recognition tasks. Here we present an iterative process of refinement for a 3D Morphable Model (3DMM) of the human ear that employs data augmentation. The process employs the following stages 1) landmark-based 3DMM fitting; 2) 3D template deformation to overcome noisy over-fitting; 3) 3D mesh editing, to improve the fit to manual 2D landmarks. These processes are wrapped in an iterative procedure that is able to bootstrap a weak, approximate model into a significantly better model. Evaluations using several performance metrics verify the improvement of our model using the proposed algorithm. We use this new 3DMM model-booting algorithm to generate a refined 3D morphable model of the human ear, and we make this new model and our augmented training dataset public.

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“…Recently, several variants of CPD have been proposed to meet specific requirements. Hu et al [10] added landmark information in the registration, while [11] and [12] focused on automatic parameter selection and outlier modeling.…”

Section: Introductionmentioning

confidence: 99%

Non-rigid point set registration for Chinese characters using structure-guided coherent point drift

Sun

Lian

Tang

et al. 2014

2014 IEEE International Conference on Image Processing (ICIP)

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This paper proposes a non-rigid point set registration method called Structure-Guided Coherent Point Drift (SGCPD). The key idea of our method is to utilize structural information and combine the global and local point registrations together to improve the original Coherent Point Drift (CPD) algorithm. Specifically, given two point sets, we first align them using the CPD method with Localized Operator (CPDLO). Then we divide the target point set into several subsets and apply CPDLO to each subset. Finally, we implement the above two procedures until convergence. In this manner, more detailed information can be well exploited and thus higher registration accuracy can be achieved. Experimental results demonstrate that our method outperforms the original CPD approach on both point registration accuracy and skeleton decomposition accuracy for Chinese characters.

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A refined coherent point drift (CPD) algorithm for point set registration

Cited by 42 publications

References 11 publications

Non-rigid 3D Shape Registration Using an Adaptive Template

Non-rigid 3D Shape Registration Using an Adaptive Template

A Data-Augmented 3D Morphable Model of the Ear

Non-rigid point set registration for Chinese characters using structure-guided coherent point drift

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