Measurement of human figure size based on laser 3D scanning
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Measurement of human figure size based on laser 3D scanning
Optics and Precision EngineeringVol. 15, Issue 1, Pages: 84-88(2007)
作者机构:
天津大学 精仪学院光电信息技术科学教育部重点实验室 天津,300072
作者简介:
基金信息:
DOI:
CLC:TN247
Received:23 April 2006,
Revised:05 September 2006,
Published Online:30 January 2007,
Published:30 January 2007
稿件说明:
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TIAN Qing-guo, GE Bao-zhen, DU Pu, et al. Measurement of human figure size based on laser 3D scanning[J]. Optics and precision engineering, 2007, 15(1): 84-88.
DOI:
TIAN Qing-guo, GE Bao-zhen, DU Pu, et al. Measurement of human figure size based on laser 3D scanning[J]. Optics and precision engineering, 2007, 15(1): 84-88.DOI:
Measurement of human figure size based on laser 3D scanning
A measuring approach for figure size from 3D human point cloud is presented. Using a light-stripe 3D laser scanning system to scan the desired pose of body
human’s three-dimensional coordinates are obtained without loss. Based on the directions of eyesight and the natural separating of two legs
the human’s orientation is adjusted to facing
z
positive direction. Applying manual method and maximal connectivity domain method to delete noisy points
6 figure points and 20 figure planes are labeled on human silhouette. Two different section planes’ y coordinates are used to obtain one length size of human and a method
called convex hull method
to measure body section’s girth
respectively. Using this technique
the 34 key figure lengths and girth sizes can be measured
and the factors affecting measurement precision can be analyzed. Measurement results show that the typical measurement error of this method is lower than 3% and can meet the requirement of industry.
关键词
Keywords
references
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Rotating point cloud segmentation and reverse reconstruction based on computed tomography images
3D point cloud classification and segmentation based on dual attention and weighted dynamic graph convolution
Object 6-DoF pose estimation using auxiliary learning
Binocular vision measurement method incorporating one-dimensional probabilistic Hough transform and local Zernike moment
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