Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Fast capture of textured full-body avatar with RGB-D cameras

Lin, Shuai, Chen, Yin, Lai, Yukun, Martin, Ralph Robert and Cheng, Zhi-Quan 2016. Fast capture of textured full-body avatar with RGB-D cameras. Visual Computer 32 (6-8) , pp. 681-691. 10.1007/s00371-016-1245-9

[img]
Preview
PDF - Accepted Post-Print Version
Download (4MB) | Preview

Abstract

We present a practical system which can provide a textured full-body avatar within three seconds. It uses sixteen RGB-depth (RGB-D) cameras, ten of which are arranged to capture the body, while six target the important head region. The configuration of the multiple cameras is formulated as a constraint-based minimum set space-covering problem, which is approximately solved by a heuristic algorithm. The camera layout determined can cover the fullbody surface of an adult, with geometric errors of less than 5 mm. After arranging the cameras, they are calibrated using a mannequin before scanning real humans. The 16 RGB-D images are all captured within 1 s, which both avoids the need for the subject to attempt to remain still for an uncomfortable period, and helps to keep pose changes between different cameras small. All scans are combined and processed to reconstruct the photo-realistic textured mesh in 2 s. During both system calibration and working capture of a real subject, the high-quality RGB information is exploited to assist geometric reconstruction and texture stitching optimization.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: Springer Verlag
ISSN: 0178-2789
Date of First Compliant Deposit: 13 April 2016
Date of Acceptance: 13 April 2016
Last Modified: 04 Jun 2017 03:33
URI: http://orca-mwe.cf.ac.uk/id/eprint/89147

Citation Data

Cited 8 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics