A robust algorithm for point set registration using mixture of Gaussians

By Bing Jian, Baba C. Vemuri. 2005

397 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions (2011, methodology): “For GMMs, we have the analytical result for their L 2 divergences (Jian and Vemuri, 2005).”
Point Cloud GAN (2018, methodology): “Instead of using divergence esitmates which requires density estimation (Jian and Vemuri, 2005; Strom et al., 2010; Eckart et al., 2015), we are interested in implicit generative models with a GAN-like objective (Goodfellow et al., 2014), which has been demonstrated to learn complicated…”
TEASER: Fast and Certifiable Point Cloud Registration (2020, methodology): “as a minimization of the Kullback-Leibler divergence between two Mixture Models [69], [70], [71], [72].”
Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration (2016, neutral): “It was shown by Jian and Vemuri [29] that if the point-sets are represented with Gaussian Mixture Models (GMMs), ICP is related to minimizing the Kullback-Leibler divergence of two GMMs.”
Go-ICP: Solving 3D Registration Efficiently and Globally Optimally (2013, methodology): “Probability density based techniques [18, 33, 27, 6] have been used to model the points with Gaussian Mixture Models.”
Closed-Form Jensen-Renyi Divergence for Mixture of Gaussians and Applications to Group-Wise Shape Registration (2009, background): “The work reported here fits into the general class of approaches that avoid explicit point correspondences for non-rigid registration through the use of divergence measures between probability distributions formed around point sets[2–4].”
Non-rigid point set registration: Coherent Point Drift (2006, methodology): “The equivalence of the regularization term with that of the Motion Coherence Theory implies that we are imposing motion coherence among the points and thus we call our method the Coherent Point Drift (CPD) method.”