A robust algorithm for point set registration using mixture of Gaussians
Bing Jian,Baba C. Vemuri +1 more
- 17 Oct 2005
- Vol. 2, pp 1246-1251
TL;DR: A novel and robust approach to the point set registration problem in the presence of large amounts of noise and outliers is proposed, which derives a closed-form expression for the L/sub 2/distance between two Gaussian mixtures, which leads to a computationally efficient registration algorithm.
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Abstract: This paper proposes a novel and robust approach to the point set registration problem in the presence of large amounts of noise and outliers. Each of the point sets is represented by a mixture of Gaussians and the point set registration is treated as a problem of aligning the two mixtures. We derive a closed-form expression for the L/sub 2/distance between two Gaussian mixtures, which in turn leads to a computationally efficient registration algorithm. This new algorithm has an intuitive interpretation, is simple to implement and exhibits inherent statistical robustness. Experimental results indicate that our algorithm achieves very good performance in terms of both robustness and accuracy.
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Citations
Point Set Registration: Coherent Point Drift
Andriy Myronenko,Xubo Song +1 more
TL;DR: A probabilistic method, called the Coherent Point Drift (CPD) algorithm, is introduced for both rigid and nonrigid point set registration and a fast algorithm is introduced that reduces the method computation complexity to linear.
Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration
TL;DR: This paper presents the first globally optimal algorithm, named Go-ICP, for Euclidean (rigid) registration of two 3D point-sets under the inline-formula notation, and derives novel upper and lower bounds for the registration error function.
Robust Point Set Registration Using Gaussian Mixture Models
Bing Jian,Baba C. Vemuri +1 more
TL;DR: This paper presents a unified framework for the rigid and nonrigid point set registration problem in the presence of significant amounts of noise and outliers, and shows that the popular iterative closest point (ICP) method and several existing point setRegistration methods in the field are closely related and can be reinterpreted meaningfully in this general framework.
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TEASER: Fast and Certifiable Point Cloud Registration
TL;DR: TEASER++ as mentioned in this paper uses a truncated least squares (TLS) cost that makes the estimation insensitive to a large fraction of spurious correspondences and provides a general graph-theoretic framework to decouple scale, rotation and translation estimation, which allows solving in cascade for the three transformations.
Go-ICP: Solving 3D Registration Efficiently and Globally Optimally
Jiaolong Yang,Hongdong Li,Yunde Jia +2 more
- 01 Dec 2013
TL;DR: This paper provides the very first globally optimal solution to Euclidean registration of two 3D point sets or two3D surfaces under the L2 error by exploiting the special structure of the underlying geometry.
References
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Andrew Fitzgibbon
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TL;DR: A new method of registering point sets is introduced that is comparable in speed to the special-purpose Iterated Closest Point algorithm, and the registration error is directly minimized using general-purpose non-linear optimization (the Levenberg–Marquardt algorithm).