The Z-axis, X-axis, Weight and Disambiguation Methods for Constructing Local Reference Frame in 3D Registration: An Evaluation
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Abstract (arXiv)
The local reference frame (LRF), as an independent coordinate system generated on a local 3D surface, is widely used in 3D local feature descriptor construction and 3D transformation estimation which are two key steps in the local method-based surface matching. There are numerous LRF methods have been proposed in literatures. In these methods, the x- and z-axis are commonly generated by different methods or strategies, and some x-axis methods are implemented on the basis of a z-axis being given. In addition, the weight and disambiguation methods are commonly used in these LRF methods. In existing evaluations of LRF, each LRF method is evaluated with a complete form. However, the merits and demerits of the z-axis, x-axis, weight and disambiguation methods in LRF construction are unclear. In this paper, we comprehensively analyze the z-axis, x-axis, weight and disambiguation methods in existing LRFs, and obtain six z-axis and eight x-axis, five weight and two disambiguation methods. The performance of these methods are comprehensively evaluated on six standard datasets with different application scenarios and nuisances. Considering the evaluation outcomes, the merits and demerits of different weight, disambiguation, z- and x-axis methods are analyzed and summarized. The experimental result also shows that some new designed LRF axes present superior performance compared with the state-of-the-art ones.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 50 references that are in the map. First posted on arXiv 17 Apr 2022.
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8 of 50 references.
References · 8
- 2020SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registrationcited by 141
- 2017Learning Compact Geometric Featurescited by 45
- 2013Rotational Projection Statistics for 3D Local Surface Description and Object Recognitioncited by 36
- 2021R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Methodcited by 26
- 2020Distinctive 3D local deep descriptorscited by 21
- 2019A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matchingcited by 10
- 2019Evaluating Local Geometric Feature Representations for 3D Rigid Data Matchingcited by 3
- 2017A Novel SDASS Descriptor for Fully Encoding the Information of 3D Local Surfacecited by 2
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