A dense 3D reconstruction approach from uncalibrated video sequences

Ling, L; Burrent, IS; Cheng, E

A dense 3D reconstruction approach from uncalibrated video sequences

Ling, L Burrent, IS

Cheng, E

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2012, 2012, pp. 587 - 592
Issue Date:: 2012-10-04

Closed Access

	Filename	Description	Size
	06266449.pdf	Published version	1.83 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Ling, L	en_US
dc.contributor.author	Burrent, IS https://orcid.org/0000-0003-3795-7722	en_US
dc.contributor.author	Cheng, E https://orcid.org/0000-0003-1632-8062	en_US
dc.date.issued	2012-10-04	en_US
dc.identifier.citation	Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2012, 2012, pp. 587 - 592	en_US
dc.identifier.isbn	9780769547299	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120254
dc.description.abstract	Current approaches for 3D reconstruction from feature points of images are classed as sparse and dense techniques. However, the sparse approaches are insufficient for surface reconstruction since only sparsely distributed feature points are presented. Further, existing dense reconstruction approaches require pre-calibrated camera orientation, which limits the applicability and flexibility. This paper proposes a one-stop 3D reconstruction solution that reconstructs a highly dense surface from an uncalibrated video sequence, the camera orientations and surface reconstruction are simultaneously computed from new dense point features using an approach motivated by Structure from Motion (SfM) techniques. Further, this paper presents a flexible automatic method with the simple interface of 'videos to 3D model'. These improvements are essential to practical applications in 3D modeling and visualization. The reliability of the proposed algorithm has been tested on various data sets and the accuracy and performance are compared with both sparse and dense reconstruction benchmark algorithms. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2012	en_US
dc.relation.isbasedon	10.1109/ICMEW.2012.108	en_US
dc.title	A dense 3D reconstruction approach from uncalibrated video sequences	en_US
dc.type	Conference Proceeding
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Professional Practice and Leadership
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Current approaches for 3D reconstruction from feature points of images are classed as sparse and dense techniques. However, the sparse approaches are insufficient for surface reconstruction since only sparsely distributed feature points are presented. Further, existing dense reconstruction approaches require pre-calibrated camera orientation, which limits the applicability and flexibility. This paper proposes a one-stop 3D reconstruction solution that reconstructs a highly dense surface from an uncalibrated video sequence, the camera orientations and surface reconstruction are simultaneously computed from new dense point features using an approach motivated by Structure from Motion (SfM) techniques. Further, this paper presents a flexible automatic method with the simple interface of 'videos to 3D model'. These improvements are essential to practical applications in 3D modeling and visualization. The reliability of the proposed algorithm has been tested on various data sets and the accuracy and performance are compared with both sparse and dense reconstruction benchmark algorithms. © 2012 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/120254