Innovative Strategies for 3D Visualisation Using Photogrammetry and 3D Scanning for Mobile Phones

3D model generation through Photogrammetry is a modern overlay of digital information representing real world objects in a virtual world. The immediate scope of this study aims at generating 3D models using imagery and overcoming the challenge of acquiring accurate 3D meshes. This research aims to achieve optimised ways to document raw 3D representations of real life objects and then converting them into retopologised, textured usable data through mobile phones. Augmented Reality (AR) is a projected combination of real and virtual objects. A lot of work is done to create market dependant AR applications so customers can view products before purchasing them. The need is to develop a product independent photogrammetry to AR pipeline, which is freely available to create independent 3D Augmented models. Although for the particulars of this research paper, the aim would be to compare and analyse different open source SDK’s and libraries for developing optimised 3D Mesh using Photogrammetry/3D Scanning which will contribute as a main skeleton to the 3D-AR pipeline. Natural disasters, global political crisis, terrorist attacks and other catastrophes have led researchers worldwide to capture monuments using photogrammetry and laser scans. Some of these objects of “global importance” are processed by companies including CyArk (Cyber Archives) and UNESCO’s World Heritage Centre, who work against time to preserve these historical monuments, before they are damaged or in some cases completely destroyed. The need is to question the significance of preserving objects and monuments, which might be of value locally to a city or town. What is done to preserve those objects? This research would develop pipelines for collecting and processing 3D data so the local communities could contribute towards restoring endangered sites and objects using their smartphones and making these objects available to be viewed in location based AR. There exist some companies, which charge relatively large amounts of money for local scanning projects. This research would contribute as a non-profitable project, which could be later, used in school curriculums, visitor attractions and historical preservation organisations all over the globe at no cost. The scope isn’t limited to furniture, museums or marketing, but could be used for personal digital archiving as well. This research will capture and process virtual objects using Mobile Phones comparing methodologies used in Computer Vision design from data conversion on Mobile phones to 3D generation, texturing and retopologising. The outcomes of this research will be used as input for generating AR, which is application independent of any industry or product.

Content

Author and article information

Contributors

Verda Munir

Euan Dempster

David Lyons

Conference

Publication date: July 2019

Publication date (Print): July 2019

Pages: 231-236

Affiliations

[0001]School of Design and Informatics

Abertay University

Dundee DD1 1HG, Scotland

Article

DOI: 10.14236/ewic/EVA2019.43

SO-VID: 07e2aa61-926b-4243-93ee-63cbae9b0abf

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Proceedings of EVA London 2019

Conference acronym: EVA 2019

Conference number:

Conference location: London, UK

Conference date: 8 - 11 July 2019

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Electronic Visualisation and the Arts

History

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/EVA2019.43

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

D. Falkingham 2016 Trying all the free Photogrammetry! https://peterfalkingham.com/2016/09/14/trying-all-the-free-photogrammetry/ retrieved 19 September 2018 )
L. BishopC. CowanM. Jancosek 2017 Photogrammetry for Games Art, Technology and Pipeline Integration for Amazing Worlds GDC 1 Mar 2017
M. DaneshmandA. HelmiE. AvotsF. NorooziF. AlisinangluH.S. ArslandJ. GorbovaR. E. HaamerC. OzcinarG. Anbardjafari 2018 3D Scanning: A Comprehensive Survey
M. Lansard 2018 The 9 best 3D scanning apps for smartphones
A. Staff 2017 The pitfalls and potential of inexpensive 3D scanning solutions https://arstechnica.com/gadgets/2017/06/reality-check-personal-3d-scanning-has-come-far-but-its-no-copy-machine-yet/ retrieved 19 September 2018 )
R. NorbregaJ. JacobA. CoelhoJ. WeberJ. RibeiroS. Ferreira 2017 Mobile location-based augmented reality applications for urban tourism storytelling IEEE 1 8
F. Remondino 2011 Heritage Recording and 3D Modeling with Photogrammetry and 3D Scanning Remote Sensing 3 6 1104 1138
K. HsiaoH. Rashvand 2015 Data modeling mobile augmented reality: Integrated mind and body rehabilitation Multimedia Tools and Applications 74 10 3543 3560
P. Moulon 2019 openMVG Github https://github.com/openMVG/openMVG retrieved 19 September 2018 )
J. H. SpaaksN. DrostJ. MaassenG. OordS. GeorgievskaS. MorC. MeijerS. Verhoeven 2018 Structure from Motion (SFM) Github https://github.com/NLeSC/structure-from-motion/ retrieved 19 September 2018 )
M. Tenney 2012 Point Clouds to Mesh in “MeshLab” CAST Technical Publications Series 10062 http://gmv.cast.uark.edu/scanning/point-clouds-to-mesh-in-meshlab/ retrieved 12 March 2019 )
S. FuhrmannF. LangguthN. MoehrleM. WaechterM. Goesele 2015 MVE—An image-based reconstruction environment Computers & Graphics 53 44 53

Comments

Comment on this article

[1] D. Falkingham 2016 Trying all the free Photogrammetry! https://peterfalkingham.com/2016/09/14/trying-all-the-free-photogrammetry/ retrieved 19 September 2018 )

[2] L. BishopC. CowanM. Jancosek 2017 Photogrammetry for Games Art, Technology and Pipeline Integration for Amazing Worlds GDC 1 Mar 2017

[3] M. DaneshmandA. HelmiE. AvotsF. NorooziF. AlisinangluH.S. ArslandJ. GorbovaR. E. HaamerC. OzcinarG. Anbardjafari 2018 3D Scanning: A Comprehensive Survey

[4] M. Lansard 2018 The 9 best 3D scanning apps for smartphones

[5] A. Staff 2017 The pitfalls and potential of inexpensive 3D scanning solutions https://arstechnica.com/gadgets/2017/06/reality-check-personal-3d-scanning-has-come-far-but-its-no-copy-machine-yet/ retrieved 19 September 2018 )

[6] R. NorbregaJ. JacobA. CoelhoJ. WeberJ. RibeiroS. Ferreira 2017 Mobile location-based augmented reality applications for urban tourism storytelling IEEE 1 8

[7] F. Remondino 2011 Heritage Recording and 3D Modeling with Photogrammetry and 3D Scanning Remote Sensing 3 6 1104 1138

[8] K. HsiaoH. Rashvand 2015 Data modeling mobile augmented reality: Integrated mind and body rehabilitation Multimedia Tools and Applications 74 10 3543 3560

[9] P. Moulon 2019 openMVG Github https://github.com/openMVG/openMVG retrieved 19 September 2018 )

[10] J. H. SpaaksN. DrostJ. MaassenG. OordS. GeorgievskaS. MorC. MeijerS. Verhoeven 2018 Structure from Motion (SFM) Github https://github.com/NLeSC/structure-from-motion/ retrieved 19 September 2018 )

[11] M. Tenney 2012 Point Clouds to Mesh in “MeshLab” CAST Technical Publications Series 10062 http://gmv.cast.uark.edu/scanning/point-clouds-to-mesh-in-meshlab/ retrieved 12 March 2019 )

[12] S. FuhrmannF. LangguthN. MoehrleM. WaechterM. Goesele 2015 MVE—An image-based reconstruction environment Computers & Graphics 53 44 53

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