NDLI: Semantic Segmentation and Labeling of 3D Garments

Content Provider	IEEE Xplore Digital Library
Author	Li Liu Ruomei Wang Fan Zhou Zhuo Su Xiaodong Fu
Copyright Year	2014
Description	Author affiliation: Yunnan Provincial Key Lab. of Comput. Technol. Applic., Kunming Univ. of Sci. & Technol., Kunming, China (Li Liu; Ruomei Wang) \|\| Nat. Eng. Res. Center of Digital Life, Sun Yat-sen Univ., Guangzhou, China (Fan Zhou; Zhuo Su; Xiaodong Fu)
Abstract	As large collections of 3D garments continue to grow, analyzing and exploring shape variations is significant but challenging. In this paper, we propose a semi-supervised learning method for semantic segmentation and labeling of 3D garments. The key idea in this work is to address the data challenge for 3D garment analysis using semi-supervised learning method which can label parts in various 3D garments. We first develop an objective function based on Conditional Random Field (CRF) model to learn the prior knowledge of garment components from a set of training examples. Then, we segment 3D garments into five component prototypes related to top, bottom, sleeve, accessory and one-piece, respectively. And we modify the Joint Boost to automatically cluster the segmented components without requiring manual parameter tuning. The purpose of our method is to relieve the manual segmentation and labeling of components in 3D garment collections. The experimental results demonstrate our method is effective and comparable to human work.
Sponsorship	IEEE
Starting Page	299
Ending Page	304
File Size	1091807
Page Count	6
File Format	PDF
e-ISBN	9781479942848
DOI	10.1109/ICDH.2014.64
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training 3D garments Three-dimensional displays Shape Shape analysis Clothing Semantics Semi-supervised learning Semisupervised learning Labeling Shape clustering Mesh segmentation
Content Type	Text
Resource Type	Article

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