A Bayesian Formulation of Coherent Point Drift

Coherent point drift is a well-known algorithm for solving point set registration problems, i.e., finding corresponding points between shapes represented as point sets. Despite its advantages over other state-of-the-art algorithms, theoretical and practical issues remain. Among theoretical issues, (...

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Vydáno v:IEEE transactions on pattern analysis and machine intelligence Ročník 43; číslo 7; s. 2269 - 2286
Hlavní autor: Hirose, Osamu
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Bayes methods
Bayesian analysis
Coherence
coherent point drift
Convergence
Drift
fast computation
Inference algorithms
Kernel
Kernels
Matrix converters
motion coherence
Non-rigid point set registration
Parameters
Rotation
Shape
Statistical inference
Three-dimensional displays
variational Bayesian inference
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Shrnutí:Coherent point drift is a well-known algorithm for solving point set registration problems, i.e., finding corresponding points between shapes represented as point sets. Despite its advantages over other state-of-the-art algorithms, theoretical and practical issues remain. Among theoretical issues, (1) it is unknown whether the algorithm always converges, and (2) the meaning of the parameters concerning motion coherence is unclear. Among practical issues, (3) the algorithm is relatively sensitive to target shape rotation, and (4) acceleration of the algorithm is restricted to the use of the Gaussian kernel. To overcome these issues and provide a different and more general perspective to the algorithm, we formulate coherent point drift in a Bayesian setting. The formulation brings the following consequences and advances to the field: convergence of the algorithm is guaranteed by variational Bayesian inference; the definition of motion coherence as a prior distribution provides a basis for interpretation of the parameters; rigid and non-rigid registration can be performed in a single algorithm, enhancing robustness against target rotation. We also propose an acceleration scheme for the algorithm that can be applied to non-Gaussian kernels and that provides greater efficiency than coherent point drift.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2020.2971687