Abstract—In order to reconstruct the three-dimensional (3D)
point cloud of a draped fabric based on a two-dimensional
fabric drape projection, the three-dimensional point clouds of
the draped fabrics were scanned with a self-built 3D scanning
device. A resampling method based on local linear embedding
(LLE) was used to represent different 3D point clouds with the
same point number and point sequence. Principal Component
Analysis (PCA) was used to reduce the dimension of the
resampled 3D point clouds. With PCA, a completed resampled
point cloud could be represented with a signature of length
fifty-seven. At last, a regression model with a two- dimensional
(2D) fabric drape projection as input was constructed and
trained to predict the signature of length fifty-seven. With the
predicted signature, the 3D point cloud of a draped fabric could
be reconstructed. The result shows that all resampled 3D point
clouds of draped fabric have the same point number and point
sequence. The errors between the reconstructed 3D point clouds
and the ground truth are all within 6.92 mm.
Index Terms—Draped fabric, 3D triangular mesh, 2D projection, PCA, deep learning.
University, Shanghai 201620, China (e-mail: email@example.com; firstname.lastname@example.org).
Yueqi Zhong is with College of Textiles, Donghua University, Shanghai 201620, China and the Key Laboratory of Textile Science & Technology of Ministry of Education, College of Textiles, Donghua University, Shanghai, 201620, China (corresponding author; e-mail: email@example.com).
Cite: Zhicai Yu, Yueqi Zhong, and Haoyang Xie, "Reconstructing the Three-Dimensional Point Cloud of a Draped Fabric Based On a Two-Dimensional Projection," International Journal of Machine Learning and Computing vol. 11, no. 6, pp. 413-417, 2021.Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).