Registration of point cloud data from a geometric optimization perspective

By Niloy J. Mitra, Natasha Gelfand, Helmut Pottmann, Leonidas J. Guibas. Eurographics Symposium on Geometry Processing, 2004

329 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Geology

View Paper: https://www.semanticscholar.org/paper/a5189f10cd405314514312a586d80b25b42fa7cc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computer Vision and Pattern Recognition

In Geology

Citations in the corpus

Geometric Back-Projection Network for Point Cloud Classification (2019, background): “robustness and e- ciency, especially for the rapidly growing needs in industry. Point cloud classication still requires more eorts to solve theoretical and practical issues. Traditional algorithms [37,29,36,45] that operate on 3D data incorporated estimated geometric information and reconstructed mo”
Exploring collections of 3D models using fuzzy correspondences (2012, background): “For example, users can see what other shapes have similar/disimilar corresponding regions or get a sense for the range of variation within that region across the collection.”
4-points congruent sets for robust pairwise surface registration (2008, background): “Partial matching is a fundamental task for registration and recognition of 3D objects [Frome et al. 2004].”
Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge (2013, methodology): “The introduction of surfaces allows to take into account holes, potential deformation between them and the introduction of well-established techniques such as thin-plate splines (Szeliski and Lavallée, 1996; Allen et al., 2003; Chui et al., 2004; Mitra et al., 2004).”
Fast and Robust Iterative Closest Point (2020, background): “Mitra et al. [22] proposed a framework that determines the alignment by minimizing a squared distance function between the two point clouds, as well as a local quadratic approximant for efﬁcient update of the alignment.”