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.
| Original language | English |
|---|---|
| Pages (from-to) | 1-8 |
| Number of pages | 8 |
| Journal | Artificial Life and Robotics |
| DOIs | |
| Publication status | Accepted/In press - Jun 23 2018 |
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Keywords
- Iterative curved surface fitting
- Least squares method
- Point cloud data
- Reverse engineering
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Artificial Intelligence
Cite this
Iterative curved surface fitting algorithm using a raster-scanning window. / Nagata, Fusaomi; Otsuka, Akimasa; Ikeda, Takeshi; Ochi, Hiroaki; Watanabe, Keigo; Habib, Maki K.
In: Artificial Life and Robotics, 23.06.2018, p. 1-8.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Iterative curved surface fitting algorithm using a raster-scanning window
AU - Nagata, Fusaomi
AU - Otsuka, Akimasa
AU - Ikeda, Takeshi
AU - Ochi, Hiroaki
AU - Watanabe, Keigo
AU - Habib, Maki K.
PY - 2018/6/23
Y1 - 2018/6/23
N2 - 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.
AB - 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.
KW - Iterative curved surface fitting
KW - Least squares method
KW - Point cloud data
KW - Reverse engineering
UR - http://www.scopus.com/inward/record.url?scp=85048891013&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048891013&partnerID=8YFLogxK
U2 - 10.1007/s10015-018-0444-z
DO - 10.1007/s10015-018-0444-z
M3 - Article
AN - SCOPUS:85048891013
SP - 1
EP - 8
JO - Artificial Life and Robotics
JF - Artificial Life and Robotics
SN - 1433-5298
ER -