CLC number: TP391.9
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-08-23
Cited: 0
Clicked: 5611
Hong-yu Wu, Xiao-wu Chen, Chen-xu Zhang, Bin Zhou, Qin-ping Zhao. Modeling yarn-level geometry from a single micro-image[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(9): 1165-1174.
@article{title="Modeling yarn-level geometry from a single micro-image",
author="Hong-yu Wu, Xiao-wu Chen, Chen-xu Zhang, Bin Zhou, Qin-ping Zhao",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="9",
pages="1165-1174",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800693"
}
%0 Journal Article
%T Modeling yarn-level geometry from a single micro-image
%A Hong-yu Wu
%A Xiao-wu Chen
%A Chen-xu Zhang
%A Bin Zhou
%A Qin-ping Zhao
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 9
%P 1165-1174
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800693
TY - JOUR
T1 - Modeling yarn-level geometry from a single micro-image
A1 - Hong-yu Wu
A1 - Xiao-wu Chen
A1 - Chen-xu Zhang
A1 - Bin Zhou
A1 - Qin-ping Zhao
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 9
SP - 1165
EP - 1174
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1800693
Abstract: Different types of cloth show distinctive appearances owing to their unique yarn-level geometrical details. Despite its importance in applications such as cloth rendering and simulation, capturing yarn-level geometry is nontrivial and requires special hardware, e.g., computed tomography scanners, for conventional methods. In this paper, we propose a novel method that can produce the yarn-level geometry of real cloth using a single micro-image, captured by a consumer digital camera with a macro lens. Given a single input image, our method estimates the large-scale yarn geometry by image shading, and the fine-scale fiber details can be recovered via the proposed fiber tracing and generation algorithms. Experimental results indicate that our method can capture the detailed yarn-level geometry of a wide range of cloth and reproduce plausible cloth appearances.
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