The Alignment of Triangular Meshes Based on the Distance Feature Between the Centroid and Vertices


KIPS Transactions on Software and Data Engineering, Vol. 11, No. 12, pp. 525-530, Dec. 2022
https://doi.org/10.3745/KTSDE.2022.11.12.525,   PDF Download:
Keywords: Alignment, Mesh, Distance Feature, Centroid, ICP, Go-ICP
Abstract

Although the iterative closest point (ICP) algorithm has been widely used to align two point clouds, ICP tends to fail when the initial orientation of the two point clouds are significantly different. In this paper, when two triangular meshes A and B have significantly different initial orientations, we present an algorithm to align them. After obtaining weighted centroids for meshes A and B, respectively, vertices that are likely to correspond to each other between meshes are set as feature points using the distance from the centroid to the vertices. After rotating mesh B so that the feature points of A and B to be close each other, RMSD (root mean square deviation) is measured for the vertices of A and B. Aligned meshes are obtained by repeating the same process while changing the feature points until the RMSD is less than the reference value. Through experiments, we show that the proposed algorithm aligns the mesh even when the ICP and Go-ICP algorithms fail.


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Cite this article
[IEEE Style]
M. Koo, S. Jeong, K. Kim, "The Alignment of Triangular Meshes Based on the Distance Feature Between the Centroid and Vertices," KIPS Transactions on Software and Data Engineering, vol. 11, no. 12, pp. 525-530, 2022. DOI: https://doi.org/10.3745/KTSDE.2022.11.12.525.

[ACM Style]
Minjeong Koo, Sanghun Jeong, and Ku-Jin Kim. 2022. The Alignment of Triangular Meshes Based on the Distance Feature Between the Centroid and Vertices. KIPS Transactions on Software and Data Engineering, 11, 12, (2022), 525-530. DOI: https://doi.org/10.3745/KTSDE.2022.11.12.525.