Least-Squares Fitting of Two 3-D Point Sets
Abstract
Two point sets {pi} and {p'i}; i = 1, 2,..., N are related by p'i = Rpi + T + Ni, where R is a rotation matrix, T a translation vector, and Ni a noise vector. Given {pi} and {p'i}, we present an algorithm for finding the least-squares solution of R and T, which is based on the singular value decomposition (SVD) of a 3 × 3 matrix. This new algorithm is compared to two earlier algorithms with respect to computer time requirements.
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- Authors
- Arun, K. S., Huang, T. S., & Blostein, S. D.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1987-09
- Volume
- PAMI-9
- Issue
- 5
- Pages
- 698-700
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W1988874269
- Cited by
- 3,952
- References
- 4
- Citations by year
- 2026: 126 · 2025: 180 · 2024: 270 · 2023: 268 · 2022: 234 · 2021: 228 · 2020: 165 · 2019: 126 · 2018: 179 · 2017: 128 · 2016: 114 · 2015: 153 · 2014: 153 · 2013: 135 · 2012: 148
- FWCI
- 15.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- least squares fitting, rigid transformation, rotation and translation estimation, singular value decomposition, point cloud registration, computational efficiency
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