September 1, 1987IEEE Transactions on Pattern Analysis and Machine Intelligence

Least-Squares Fitting of Two 3-D Point Sets

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Authors

KAK.S. ArunCochin University of Science and Technology THThomas S. HuangNorthwestern University SBSteven D. BlosteinQueen's University

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Implication

Comparative analysis demonstrates least-squares fitting via singular value decomposition in paired 3-D point sets, highlighting reduced computer time requirements.

Key Points

Singular value decomposition of a 3 × 3 matrix yields an exact least squares fitting solution for paired point sets, resolving rotation matrix and translation vector shifts.
Algorithm comparison measures the computational efficiency of the singular value decomposition approach, evaluating computer time requirements against two earlier methods.
May enable faster point sets alignment in three dimensions, improving rigid body transformation estimates when coordinates are corrupted by an additive noise vector.

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Arun et al. (1987) studied this question.

synapsesocial.com/papers/693759d88bd949e9baaf2eaa https://doi.org/10.1109/tpami.1987.4767965

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Implication

Comparative analysis demonstrates least-squares fitting via singular value decomposition in paired 3-D point sets, highlighting reduced computer time requirements.

Least-Squares Fitting of Two 3-D Point Sets

Discussion

Member takes

Implication

Key Points

Cite This Study

Least-Squares Fitting of Two 3-D Point Sets

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider