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Candidate-based matching of 3-D point clouds with axially switching pose estimation

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In 3-D pattern matching, a reconstructed 3-D data point cloud of an object can be matched with the 3-D model point cloud to determine the object pose by employing the iterative closest point (ICP) algorithm. Typical ICP algorithm is time-consuming and may get stuck in local minimum if the pose difference between the two point clouds is not small enough. To resolve this uncertainty problem and enhance the matching capability, the candidate-based axially switching (CBAS) computed closer point (CCP) approach is proposed which is efficient, effective, and robust. It is based on evaluating origin candidates in the model point cloud to determine the approximate pose of the data point cloud. The proposed CBAS-CCP approach allows large uncertainty of the pose difference between the model and the object. The applicability and effectiveness of the proposed approach has been successfully validated by experimenting with 3-D data of real objects.

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This research was supported by Industrial Technology Research Institute, Taiwan, ROC under Grant D301AA4111-FY103.


This study was funded by Industrial Technology Research Institute, Taiwan, ROC (D301AA4111-FY103).

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Correspondence to Wen-Chung Chang.

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Chang, W., Wu, C. Candidate-based matching of 3-D point clouds with axially switching pose estimation. Vis Comput 36, 593–607 (2020). https://doi.org/10.1007/s00371-019-01642-5

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  • Computed closer point (CCP)
  • Iterative closest point (ICP)
  • KD-tree
  • Point cloud
  • 3-D registration