Least squares 3D surface and curve matching

By Armin Gruen, Devrim Akca. 2005

633 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology

View Paper: https://www.semanticscholar.org/paper/f9c59438397e92ebecb04bc99886505dcb96fd31

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Geology

Citations in the corpus

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.”
Deformation measurement using terrestrial laser scanning data and least squares 3D surface matching (2008, methodology): “The core of the procedure is the advanced least squares surface matching algorithm proposed by Gruen and Akca (2005). The proposed procedure takes full advantage of the geometric information contained in the multi-temporal TLS point clouds, and offers, at the same time, a flexible tool to be used in”
Towards 3D Point cloud based object maps for household environments (2008, methodology): “Furthermore, methods for generating initial approximate alignments [10,35,19], as well as choosing alternate optimization methods [14,6], improved the results of the registration process.”
Aligning point cloud views using persistent feature histograms (2008, background): “narrow convergence basin, several alternative optimization methods have been proposed [18], [19], though they require that the individual point cloud data views are already roughly aligned with each other.”
Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013, methodology): “Independently, Gruen and Akca [67] proposed a similar method based on a generalized Gauss-Markov model which uses a statistical framework to model noise.”
Heritage Recording and 3D Modeling with Photogrammetry and 3D Scanning (2011, methodology): “The scans registration is generally done in two steps: (a) a pair-wise manual or automatic coarse alignment using targets or few homologues features and (b) final global alignment based on iterative closest points [112] or least squares method procedures [113].”
Fast Point Feature Histograms (FPFH) for 3D registration (2009, methodology): “The ICP method has seen many improvements from its original form, from using non-linear optimization methods [7], [8], finding good initial guesses [9], [10], or es-”