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Oct.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2022  >  9(10): 1861-1878
J. Q. Yang, Z. Q. Huang, S. W. Quan, Z. G. Cao, and Y. N. Zhang, “RANSACs for 3D rigid registration: A comparative evaluation,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1861–1878, Oct. 2022. doi: 10.1109/JAS.2022.105500
Citation: J. Q. Yang, Z. Q. Huang, S. W. Quan, Z. G. Cao, and Y. N. Zhang, “RANSACs for 3D rigid registration: A comparative evaluation,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1861–1878, Oct. 2022. doi: 10.1109/JAS.2022.105500
shu

RANSACs for 3D Rigid Registration: A Comparative Evaluation

doi: 10.1109/JAS.2022.105500
  • 1.

    National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China

  • 2.

    School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China

  • 3.

    School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China

Funds:  This work was supported in part by the National Natural Science Foundation of China (NFSC) (62002295, U19B2037), China Postdoctoral Science Foundation (2020M673319), Shaanxi Provincial Key R & D Program (2021KWZ-03), and the Natural Science Basic Research Plan in Shaanxi Province of China (2021JQ-290, 2020JQ-210)
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    Abstract
  • Estimating an accurate six-degree-of-freedom (6-DoF) pose from correspondences with outliers remains a critical issue to 3D rigid registration. Random sample consensus (RANSAC) and its variants are popular solutions to this problem. Although there have been a number of RANSAC-fashion estimators, two issues remain unsolved. First, it is unclear which estimator is more appropriate to a particular application. Second, the impacts of different sampling strategies, hypothesis generation methods, hypothesis evaluation metrics, and stop criteria on the overall estimators remain ambiguous. This work fills these gaps by first considering six existing RANSAC-fashion methods and then proposing eight variants for a comprehensive evaluation. The objective is to thoroughly compare estimators in the RANSAC family, and evaluate the effects of each key stage on the eventual 6-DoF pose estimation performance. Experiments have been carried out on four standard datasets with different application scenarios, data modalities, and nuisances. They provide us with input correspondence sets with a variety of inlier ratios, spatial distributions, and scales. Based on the experimental results, we summarize remarkable outcomes and valuable findings, so as to give practical instructions to real-world applications, and highlight current bottlenecks and potential solutions in this research realm.

     

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    Highlights

    • A survey of six existing 6-DoF pose estimators in the RANSAC family
    • Eight RANSAC variants for a comprehensive 3D registration evaluation
    • Summary of the merits and demerits of RANSACs for 3D rigid registration

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