Modeling yarn-level geometry from a single micro-image

Hong-yu WU; Xiao-wu CHEN; Chen-xu ZHANG; Bin ZHOU; Qin-ping ZHAO

doi:10.1631/FITEE.1800693

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Modeling yarn-level geometry from a single micro-image

Special Feature on Virtual Reality | Updated：2022-05-19

- Modeling yarn-level geometry from a single micro-image
- 基于单幅微距图像的布料丝线结构建模
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 9, Pages: 1165-1174(2019)
- Affiliations：
  
  State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing 100191, China
- Author bio：
  
  Hong-yu WU, whyvrlab@buaa.edu.cn
  [ "Xiao-wu CHEN, chen@buaa.edu.cn" ]
  [ "Chen-xu ZHANG, zhangchenxu528@buaa.edu.cn" ]
  [ "Bin Zhou, zhoubin@buaa.edu.cn" ]
- Funds：
- DOI：10.1631/FITEE.1800693
  CLC： TP391.9
- Received：04 November 2018，
  
  Revised：2019-;8-23，
  
  Published：2019-09
- Accepted：
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Hong-yu WU, Xiao-wu CHEN, Chen-xu ZHANG, et al. Modeling yarn-level geometry from a single micro-image[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(9): 1165-1174.
DOI：

Hong-yu WU, Xiao-wu CHEN, Chen-xu ZHANG, et al. Modeling yarn-level geometry from a single micro-image[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(9): 1165-1174. DOI： 10.1631/FITEE.1800693.

摘要

真实世界的布料具有不同的微观丝线结构，导致不同布料具有各种各样外观。布料真实感绘制在影视制作、电子商务等领域具有重要应用价值。为获取布料微观丝线几何信息，传统方法需要微米CT扫描仪等昂贵复杂设备，并且采集过程费时费力，难以普及。为降低布料丝线获取复杂度，本文提出一种基于单幅微距图像的丝线获取与建模方法，仅需装有微距镜头的普通消费级相机拍摄的微距图像。该方法首先通过单幅微距图像的明暗信息获得丝线的大尺度几何；然后，通过丝线追踪算法获得丝线上的纤维细节；最后将这两者结合，得到布料丝线的微观尺度几何。实验结果表明，本方法能够高效获取各种类型布料丝线几何。

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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