Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (04): 123-128.doi: 10.13475/j.fzxb.20190600806

• Apparel Engineering • Previous Articles     Next Articles

Image-based three-dimensional garment reconstruction

PAN Bo1, ZHONG Yueqi1,2()   

  1. 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 E-mail:zhyq@dhu.edu.cn

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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

CLC Number: 

  • TS942.8

Fig.1

Garment reconstruction result display. (a) Sparse point cloud; (b) Dense point cloud; (c) Mesh; (d) Texture mapping"

Fig.2

Incremental structure from motion pipeline"

Fig.3

Poission reconstruction results by different octree depth"

Fig.4

Mesh reconstruction error. (a) Initial dense point cloud; (b) Reconstructed mesh effect before improvement; (c) Dense point after fusion;(d) Mesh effect after improvement"

Fig.5

Different wearing types during images shooting"

Fig.6

Realization of dense point cloud registration. (a) Dense point cloud before cropping; (b) Dense point cloud after cropping; (c) Reconstructed mesh after fusion;(d) Dense point cloud overall effect after registration"

Fig.7

Shooting scheme. (a) Ring style;(b) Hemisphere style"

Fig.8

Reconstruction effect comparison. (a) Ring/40; (b)Ring/60; (c) Ring/80;(d) Ring/100; (e) Hemisphere/80"

Fig.9

Point cloud comparison on shoulder parts with different shooting scheme. (a) Ring;(b) Hemisphere"

Fig.10

Reconstruction result error distribution"

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