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Reference data generation for evaluating pairwise registration algorithms
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DOI：10.1016/j.measurement.2025.118602.png)
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Abstract
En 中文
• Unconstrained data for evaluating registration leads to biased performance. • Efficient null-space method generates reference data from the problem formulation. • Bounded convex optimization resolves ill-defined data generation. • Low bias in reference data reduces uncertainty to improve algorithms’ evaluation.
Keywords:
Reference data
Iterative Closest Point
Registration
Point cloud
Machine vision
Uncertainty
Journal
IF:
5.6
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2.0W
Citations:
5.4W
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