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Title: KSS-ICP: point cloud registration based on Kendall shape space
Authors: Lv, Chenlei
Lin, Weisi
Zhao, Baoquan
Keywords: Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
Issue Date: 2023
Source: Lv, C., Lin, W. & Zhao, B. (2023). KSS-ICP: point cloud registration based on Kendall shape space. IEEE Transactions On Image Processing, 32, 1681-1693.
Project: MOE2021
Journal: IEEE Transactions on Image Processing 
Abstract: Point cloud registration is a popular topic that has been widely used in 3D model reconstruction, location, and retrieval. In this paper, we propose a new registration method, KSS-ICP, to address the rigid registration task in Kendall shape space (KSS) with Iterative Closest Point (ICP). The KSS is a quotient space that removes influences of translations, scales, and rotations for shape feature-based analysis. Such influences can be concluded as the similarity transformations that do not change the shape feature. The point cloud representation in KSS is invariant to similarity transformations. We utilize such property to design the KSS-ICP for point cloud registration. To tackle the difficulty to achieve the KSS representation in general, the proposed KSS-ICP formulates a practical solution that does not require complex feature analysis, data training, and optimization. With a simple implementation, KSS-ICP achieves more accurate registration from point clouds. It is robust to similarity transformation, non-uniform density, noise, and defective parts. Experiments show that KSS-ICP has better performance than the state-of-the-art. Code1 and executable files2 are made public.
ISSN: 1057-7149
DOI: 10.1109/TIP.2023.3251021
Schools: School of Computer Science and Engineering 
Rights: © 2023 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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