Chinese Optics, Volume. 18, Issue 3, 487(2025)
Improved RANSAC hypothesis evaluation metrics for point cloud registration
[3] [3] HUANG X SH, MEI G F, ZHANG J, et al. A comprehensive survey on point cloud registration[J]. arXiv preprint arXiv: 2103.02690, 2021.
[4] [4] WONG J M, KEE V, LE T, et al. SegICP: integrated deep semantic segmentation pose estimation[C]. Proceedings of 2017 IEEERSJ International Conference on Intelligent Robots Systems, IEEE, 2017: 57845789.
[9] [9] LU W X, WAN G W, ZHOU Y, et al. DeepVCP: an endtoend deep neural wk f point cloud registration[C]. Proceedings of 2019 IEEECVF International Conference on Computer Vision, IEEE, 2019: 1221.
[10] [10] HUANG SH Y, GOJCIC Z, USVYATSOV M, et al. PREDAT: registration of 3D point clouds with low overlap[C]. Proceedings of 2021 IEEECVF Conference on Computer Vision Pattern Recognition, IEEE, 2021: 42674276.
[11] [11] BAI X Y, LUO Z X, ZHOU L, et al. D3Feat: joint learning of dense detection deion of 3D local features[C]. Proceedings of 2020 IEEECVF Conference on Computer Vision Pattern Recognition, IEEE, 2020: 63596367.
[12] [12] CHOY C, PARK J, KOLTUN V. Fully convolutional geometric features[C]. Proceedings of 2019 IEEECVF International Conference on Computer Vision, IEEE, 2019: 89588966.
[14] [14] CENSI A. An ICP variant using a pointtoline metric[C]. Proceedings of 2008 IEEE International Conference on Robotics Automation, IEEE, 2008: 1925.
[16] [16] FÖRSTNER W, KHOSHELHAM K. Efficient accurate registration of point clouds with plane to plane crespondences[C]. Proceedings of 2017 IEEE International Conference on Computer Vision Wkshops, IEEE, 2017: 21652173.
[17] [17] SEGAL A V, HAEHNEL D, THRUN S. GeneralizedICP[M]TRINKLE J, MATSUOKA Y, CASTELLANOS J A. Robotics: Science Systems V. Cambridge: MIT Press, 2010: 435.
[18] [18] RUSU R B, COUSINS S. 3D is here: point cloud library (PCL)[C]. Proceedings of 2011 IEEE International Conference on Robotics Automation, IEEE, 2011: 14.
[19] [19] BAI X Y, LUO Z X, ZHOU L, et al. PointDSC: robust point cloud registration using deep spatial consistency[C]. Proceedings of 2021 IEEECVF Conference on Computer Vision Pattern Recognition, IEEE, 2021: 1585415864.
[20] [20] CHOY C, DONG W, KOLTUN V. Deep global registration[C]. Proceedings of 2020 IEEECVF Conference on Computer Vision Pattern Recognition, IEEE, 2020: 25142523.
[21] [21] PAIS G D, RAMALINGAM S, GOVINDU V M, et al. 3DReg: a deep neural wk f 3D point registration[C]. Proceedings of 2020 IEEECVF Conference on Computer Vision Pattern Recognition, IEEE, 2020: 71937203.
[26] [26] ZHANG X Y, YANG J Q, ZHANG SH K, et al. 3D registration with maximal cliques[C]. Proceedings of 2023 IEEECVF Conference on Computer Vision Pattern Recognition, IEEE, 2023: 1774517754.
[27] [27] CHEN ZH, SUN K, YANG F, et al. SC2PCR: a second der spatial compatibility f efficient robust point cloud registration[C]. Proceedings of 2022 IEEECVF Conference on Computer Vision Pattern Recognition, IEEE, 2022: 1322113231.
[28] [28] RUSU R B, BLODOW N, BEETZ M. Fast point feature histograms (FPFH) f 3D registration[C]. Proceedings of 2009 IEEE International Conference on Robotics Automation, IEEE, 2009: 32123217.
[29] [29] RUSU R B, BLODOW N, MARTON Z C, et al. Aligning point cloud views using persistent feature histograms[C]. Proceedings of 2008 IEEERSJ International Conference on Intelligent Robots Systems, IEEE, 2008: 33843391.
Get Citation
Copy Citation Text
Si-hao YU, Shao-yan GAI, Fei-peng DA. Improved RANSAC hypothesis evaluation metrics for point cloud registration[J]. Chinese Optics, 2025, 18(3): 487
Category:
Received: Nov. 15, 2024
Accepted: Dec. 24, 2024
Published Online: Jun. 16, 2025
The Author Email: