Abstract
This paper presents a new methodology for aligning three-dimensional (3D) models of objects, based on point correspondences. In this case, objects are modelled as 3D point clouds. The proposed methodology considers pairs of such point clouds and firstly down-samples them in order to further improve processing time. Then, corresponding points are allocated between the processed point clouds, by using a novel combinational descriptor scheme. Finally, a global transformation is estimated from the inliers of the obtained correspondences. This transformation is used to align the two point clouds. The proposed methodology was applied to five pairs of large scale 3D point clouds. Results indicate that the proposed scheme achieved satisfactory alignment accuracy for all tested data pairs.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
