SPROSAC: Streamlined progressive sample consensus for coarse–fine point cloud registration
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10489-024-05400-6.pdf
Reference59 articles.
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2. Feng HJ et al (2021) A novel feature-guided trajectory generation method based on point cloud for robotic grinding of freeform welds, (in English). Int. J. Adv. Manuf. Technol 115(5–6):1763–1781. https://doi.org/10.1007/s00170-021-07095-2
3. Lee WH, Lee KH, Lee JM, Nam BW (2020) Registration method for maintenance-work support based on augmented-reality-model generation from drawing data," (in English). J. Comput. Des. Eng. 7(6):775–787. https://doi.org/10.1093/jcde/qwaa056
4. Yue XF, Liu ZY, Zhu J, Gao XL, Yang BJ, Tian YS (2022) Coarse-fine point cloud registration based on local point-pair features and the iterative closest point algorithm, (in English). Appl. Intell. 52(11):12569–12583. https://doi.org/10.1007/s10489-022-03201-3
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