Stitching Locally Fitted T-Splines for Fast Fitting of Large-Scale Freeform Point Clouds

Jian Wang, Sheng Bi, Wenkang Liu, Liping Zhou, Tukun Li, Iain Macleod, Richard K. Leach

Research output: Contribution to journalArticlepeer-review

Abstract

Parametric splines are popular tools for precision optical metrology of complex freeform surfaces. However, as a promising topologically unconstrained solution, existing T-spline fitting techniques, such as improved global fitting, local fitting, and split-connect algorithms, still suffer the problems of low computational efficiency, especially in the case of large data scales and high accuracy requirements. This paper proposes a speed-improved algorithm for fast, large-scale freeform point cloud fitting by stitching locally fitted T-splines through three steps of localized operations. Experiments show that the proposed algorithm produces a three-to-eightfold efficiency improvement from the global and local fitting algorithms, and a two-to-fourfold improvement from the latest split-connect algorithm, in high-accuracy and large-scale fitting scenarios. A classical Lena image study showed that the algorithm is at least twice as fast as the split-connect algorithm using fewer than 80% control points of the latter.
Original languageEnglish
Article number9816
Number of pages15
JournalSensors
Volume23
Issue number24
DOIs
Publication statusPublished - 14 Dec 2023

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