Abstract
To fill the binary image of draped fabric into a comparable grayscale image with detailed shade information, the three-dimensional point cloud of draped fabric was obtained with a self-built three-dimensional scanning device. The three-dimensional point cloud of drape fabric is encapsulated into a triangular mesh, and the binary and grayscale images of draped fabric were rendered in virtual environments separately. A pix2pix convolutional neural network with the binary image of draped fabric as input and the grayscale image of draped fabric as output was constructed and trained. The relationship between the binary image and the grayscale image was established. The results show that the trained pix2pix neural network can fill unknown binary top view images of draped fabric to grayscale images. The average pixel cosine similarity between filling results and ground truth could reach 0.97.
Highlights
IntroductionThe research of fabric drape is beneficial to the design of fabric and provides a significant reference for fabric inquiry
Fabric drape performance is a special expression of fabric style in vision
We propose to fill the binary images of draped fabric into grayscale images which have a uniform grayscale
Summary
The research of fabric drape is beneficial to the design of fabric and provides a significant reference for fabric inquiry. There are rare reports about the comprehensive comparative analysis of fabric drape performance. One important aspect which is rarely studied is fabric inquiry based on fabric drape performance. How to find one or more fabric(s), which have the same drape performance with a given fabric sample, from a fabric sample library? Matching with the existing fabric drape indicators is a feasible method, but the disadvantage of this method is that these indicators can only characterize several single aspects of fabric drape performance. The drape result of the same fabric under the same
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