A Correlation-Based Approach to Robust Point Set Registration

By Yanghai Tsin, Takeo Kanade. European Conference on Computer Vision, 2004

463 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/583fb42232a78b4dca880b70b8d8bbe4297fe90e

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Non-Rigid Point Set Registration by Preserving Global and Local Structures (2015, methodology): “In the recent past, the point registration is typically solved by probabilistic methods [11], [12], [18], [19].”
Neighbors Do Help: Deeply Exploiting Local Structures of Point Clouds (2017, methodology): “It has been shown that kernel correlation as a function of pairwise-point-distance is an efﬁcient way to measure geometric afﬁnity between 2D/3D point sets and has been used in point cloud registration and feature correspondence problems [9,23,28].”
Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration (2016, methodology): “Although improved robustness to outliers and poor initializations could be achieved by GMM-based techniques [17], [29], [30], [31], the optimization was still based on local search.”
Point Set Registration: Coherent Point Drift (2009, methodology): “In fact, several rigid point set methods, including Joshi and Lee [11], Wells [12], Cross and Hancock [13], Luo and Hancock [6], [14], McNeill and Vijayakumar [15] and Sofka et al. [16], explicitly formulate the point sets registration as a maximum likelihood (ML) estimation problem, to ﬁt the GMM…”
A review of multimodal image matching: Methods and applications (2021, methodology): “Many other density-based and optimization-based methods are introduced for robustness and efficiency enhancement, such as kernel density method [290], support vector parameterized strategy [291], or fuzzy clustering method [292], while optimization-based methods include stochastic optimization appro”
Deformable Medical Image Registration: A Survey (2013, background): “Tsin and Kanade [298] proposed to register point sets based on a measure called kernel correlation.”
A robust algorithm for point set registration using mixture of Gaussians (2005, methodology): “It turns out that the kernel correlation (KC) based point set registration approach introduced recently by Tsin and Kanade [8] also fits into this framework.”
Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge (2013, methodology): “Other methods exist, based for instance on the Least-squares procedure (Gruen and Akca, 2005; Grant et al., 2012), the Random Sample Consensus algorithm (Chen et al., 1999), kernel correlation (Tsin and Kanade, 2004) or the Normal Distribution Transform (Ripperda and Brenner, 2005) exist.”
Image Matching from Handcrafted to Deep Features: A Survey (2020, methodology): “Tsin and Kanade (2004) were the first to propose such a method and used kernel density functions to model the point sets, and the discrepancy measure is defined as kernel correlation.”
RPM-Net: Robust Point Matching Using Learned Features (2020, background): “Alternatively, the basin of convergence of ICP can be widened using soft assignment strategies [6, 12, 33].”
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling (2017, unknown): “It has been shown that kernel correlation as a function of pairwise point distance is an efﬁcient way to measure geometric afﬁnity between 2D/3D point sets and has been used in point cloud registration and feature correspondence problems [17, 33, 38].”
Non-rigid point set registration: Coherent Point Drift (2006, methodology): “A correlation-based approach to point set registration is proposed in [8].”
Articulated and Generalized Gaussian Kernel Correlation for Human Pose Estimation (2016, methodology): “The algorithms in the second group directly optimize an energy function without involving correspondences, including density alignment [26] and kernel correlation [18].”
Fast Global Registration (2016, background): “Other works explored the utility of relaxed assignments [14, 24, 32], distance field representations [6], and mixture models [21, 41] for increasing the robustness of local registration.”
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.”