基于二维图像的三维服装重建

doi:10.13475/j.fzxb.20190600806

纺织学报 ›› 2020, Vol. 41 ›› Issue (04): 123-128.doi: 10.13475/j.fzxb.20190600806

基于二维图像的三维服装重建

潘博¹, 钟跃崎¹^,²()

1.东华大学纺织学院, 上海 201620
2.东华大学纺织面料与技术教育部重点实验室, 上海 201620

收稿日期:2019-06-04 修回日期:2020-01-15 出版日期:2020-04-15 发布日期:2020-04-27
通讯作者: 钟跃崎
作者简介:潘博(1995—),男,硕士生。主要研究方向为基于二维图像的三维对象重建技术。
基金资助:
国家自然科学基金项目(61572124)

Image-based three-dimensional garment reconstruction

PAN Bo¹, ZHONG Yueqi¹^,²()

1. College of Textiles, Donghua University, Shanghai 201620, China
2. Key Laboratory of Textile Science & Technology, Ministry of Education, Donghua University, Shanghai 201620, China

Received:2019-06-04 Revised:2020-01-15 Online:2020-04-15 Published:2020-04-27
Contact: ZHONG Yueqi

摘要/Abstract

摘要：

针对泊松重建过程中点云缺失导致曲面错误重构的问题,提出采用最近点迭代技术对分批重建点云实现配准融合,并以此恢复三维结构的解决策略。通过对比不同拍摄方案和图像数量对模型重建效果的影响,确定了合适的重建图像数量与拍摄方案;对泊松重建八叉树深度的最优参数选择进行分析,并在此基础上对重建模型精度进行探究。结果表明:泊松重建八叉树深度为11时,可还原模型表面细节;图像数量大于60,且采用半球式拍摄方案更有利于模型的完整性;以深度相机扫描获取点云作为基准,最终获取的三维模型误差小于5.8 mm。

关键词: 泊松重建, 二维图像, 三维重建, 点云融合, 最近点迭代技术, 八叉树深度, 服装虚拟展示

Abstract:

Due to mesh reconstruction error caused by the lack of point cloud during the Poisson reconstruction, a solution strategy that fuse dense point cloud reconstructed in batches by using the nearest iteration algorithm was proposed to restore three-dimensional(3-D) structure. The appropriate number of reconstructed images and shooting schemes were determined by comparing the effect of reconstructed dense point cloud model. The optimal parameter selection of octree depth during Possion reconstruction were analyzed, and the model accuracy based on these strategy was tested. The results indicate that it is sufficient to recover model surface details when octree depth has been set up to 11. The model demonstrates more integrity when the image quantity is greater than 60. Adopting "Hemispherical" shooting scheme has been proven effective in enhancing the model integrity. The error of final 3-D model is less than 5.8 mm by taking the point cloud obtained using depth camera sensor as a benchmark.

Key words: Poisson reconstruction, two-dimensional image, three-dimensional reconstruction, point cloud fusion, nearest iteration algorithm, octree depth, virtual display of garment

中图分类号:

TS942.8

潘博, 钟跃崎. 基于二维图像的三维服装重建[J]. 纺织学报, 2020, 41(04): 123-128.

PAN Bo, ZHONG Yueqi. Image-based three-dimensional garment reconstruction[J]. Journal of Textile Research, 2020, 41(04): 123-128.

图/表 10

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

参考文献 14

[1]	董鹏辉, 柯良军. 基于图像的三维重建技术综述[J]. 无线电通信技术, 2019(2):115-119.
	DONG Penghui, KE Liangjun. Overview of 3D reconstruction techniques based on images[J]. Radio Communications Technology, 2019(2):115-119.
[2]	AGARWAL S, SNAVELY N, SIMON I, et al. Building rome in a day[J]. Communications of the ACM, 2011,54(10):105-112.
[3]	SHEN T, ZHU S, FANG T, et al. Graph-based consistent matching for structure-from-motion [C]//European conference on computer vision. Berlin: Springer, 2016: 139-155.
[4]	YANG Xiaobo, HUANG Xiubao. Wavelet analysis of fabric surface wrinkle and self-organized neural network grade assessment[J]. Journal of Image & Graphics, 2005,10(4):473-478.
[5]	WOODHAM B P. Photometric method for determining surface orientation from multiple images[J]. Optical Engineering, 1980,19(1):1-22.
[6]	沙莎, 蒋高明. 纬编针织物三维模拟技术的研究现状与发展趋势[J]. 纺织学报, 2016,37(11):166-172.
	SHA Sha, JIANG Gaoming. Research status and development trend of 3D simulation technology for weft knitted fabric[J]. Journal of Textile Research, 2016,37(11):166-172.
[7]	张家瑞. 多视图3D场景重建算法及应用[D]. 西安:西安电子科技大学, 2018: 19-29.
	ZHANG Jiarui. Multiple view 3D reconstruction algorithm and application[D]. Xi'an: Xi'an Xidian University, 2018: 19-29.
[8]	于明, 齐菲菲, 于洋, 等. 基于立体视觉的三维重建算法[J]. 计算机工程与设计, 2013(2):358-361.
	YU Ming, QI Feifei, YU Yang, et al. Research of the algorithm for three-dimensional reconstruction based on stereo vision[J]. Computer Engineering and Design, 2013(2):358-361.
[9]	张浩鹏, 魏全茂, 张威, 等. 基于序列图像的空间目标三维重建[J]. 北京航空航天大学学报, 2016,42(2):273-279.
	ZHANG Haopeng, WEI Quanmao, ZHANG Wei, et al. Sequential-image-based space object 3D reconstruc-tion[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016,42(2):273-279.
[10]	BIANCO S, CIOCCA G, MARELLI D. Evaluating the performance of structure from motion pipelines[J]. Journal of Imaging, 2018,4(8):98.
[11]	GOESELE M, SNAVELY N, CURLESS B, et al. Multi-view stereo for community photo collections [C]//International conference on computer vision. New York: IEEE, 2007: 1-8.
[12]	FURUKAWA Y, CURLESS B, SEITZ S M, et al. Towards internet-scale multiview stereo [C]//Computer society conference on computer vision and pattern recognition. New York: IEEE, 2010: 1434-1441.
[13]	FERRARI V, TUYTELAARS T, GOOL L V. Simultaneous object recognition and segmentation by image exploration [C]//European conference on computer vision. Berlin: Springer, 2004: 40-54.
[14]	KAZHDAN M, BOLITHO M, HOPPE H. Poisson surface reconstruction [C]//Eurographics symposium on geometry processing. Goslar: Eurographics Association, 2006: 61-70.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

基于二维图像的三维服装重建

Image-based three-dimensional garment reconstruction

RichHTML

PDF (PC)

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 14

相关文章 3

Metrics

本文评价

推荐阅读 0

[1]	孟想辛斌杰李佳平. 采用多角度成像技术的纺织品三维轮廓重建算法[J]. 纺织学报, 2018, 39(04): 144-150.
[2]	程杰陈利. 利用激光扫描点云的碳纤维织物表面三维模型重建[J]. 纺织学报, 2016, 37(4): 54-59.
[3]	骆顺华王建萍. 基于二维图像非接触式人体测量方法探析[J]. 纺织学报, 2013, 34(8): 151-0.