Iterative Curved Surface Fitting Algorithm Using a Raster Scanning Window
Funding Number
JP16K06203
Author's Department
Mechanical Engineering Department
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https://link.springer.com/article/10.1007/s10015-018-0444-z
Document Type
Research Article
Publication Title
Artificial Life and Robotics, Springer
Publication Date
6-30-2018
doi
10.1007/s10015-018-0444-z
Abstract
In this paper, an iterative curved surface fitting method using a small sliding window is first proposed to smooth the original organized point cloud data (PCD) with noise and fluctuation. Samples included in a small sliding window positioned in PCD are successively fitted to a quadratic surface from upper left to lower right using a least squares method. In the iterative process, outliers of samples are asymptotically removed based on an evaluation index. This proposed method allows original PCD to be smoothed keeping its own shape feature. Then, the already developed stereolithography (STL) generator is used to produce triangulated patches from the smoothed PCD. The process allows to reconstruct 3D digital data of a real object written with STL format for reverse engineering from original PCD with noise. The effectiveness and usefulness of the proposed curved surface fitting method are demonstrated through actual smoothing experiments.
First Page
359
Last Page
366
Recommended Citation
APA Citation
Habib, M. K.
(2018). Iterative Curved Surface Fitting Algorithm Using a Raster Scanning Window. Artificial Life and Robotics, Springer, 23(3), 359–366.
10.1007/s10015-018-0444-z
https://fount.aucegypt.edu/faculty_journal_articles/131
MLA Citation
Habib, Maki Khalil
"Iterative Curved Surface Fitting Algorithm Using a Raster Scanning Window." Artificial Life and Robotics, Springer, vol. 23,no. 3, 2018, pp. 359–366.
https://fount.aucegypt.edu/faculty_journal_articles/131