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3D garment digitisation for virtual wardrobe using a commodity depth sensor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

A practical garment digitisation should be efficient and robust to minimise the cost of processing a large volume of garments manufactured in every season. In addition, the quality of a texture map needs to be high to deliver a better user experience of VR/AR applications using garment models such as digital wardrobe or virtual fitting room. To address this, we propose a novel pipeline for fast, low-cost, and robust 3D garment digitisation with minimal human involvement. The proposed system is simply configured with a commodity RGB-D sensor (e.g. Kinect) and a rotating platform where a mannequin is placed to put on a target garment. Since a conventional reconstruction pipeline such as Kinect Fusion (KF) tends to fail to track the correct camera pose under fast rotation, we modelled the camera motion and fed this as a guidance of the ICP process in KF. The proposed method is also designed to produce a high-quality texture map by stitching the best views from a single rotation, and a modified shape from silhouettes algorithm has been developed to extract a garment model from a mannequin.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
PublisherIEEE
ISBN (Electronic)978-1538610343
ISBN (Print)978-1538610350
DOIs
Publication statusPublished - 23 Jan 2018
EventInternational Conference on Computer Vision: ICCV 2017 - Venice, Italy
Duration: 22 Oct 201729 Oct 2017
http://iccv2017.thecvf.com/

Publication series

NameIEEE ICCVW Proceedings Series
PublisherIEEE
ISSN (Electronic)2473-9944

Conference

ConferenceInternational Conference on Computer Vision
CountryItaly
CityVenice
Period22/10/1729/10/17
Internet address

Documents

  • 05

    Rights statement: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    Accepted author manuscript (Post-print), 2.39 MB, PDF document

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