Identity statement area
Reference TypeConference Proceedings
Last Update2011:
Metadata Last Update2020: administrator
Citation KeyMarroquimPfeiCarvOliv:2011:Te3DMo
TitleTexturing 3D models with low geometric features
FormatDVD, On-line.
DateAug. 28 - 31, 2011
Access Date2020, Dec. 01
Number of Files1
Size5433 KiB
Context area
Author1 Marroquim, Ricardo
2 Pfeiffer, Gustavo
3 Carvalho, Felipe Moura de
4 Oliveira, Antonio Alberto Fernades de
Affiliation1 UFRJ
EditorLewiner, Thomas
Torres, Ricardo
Conference NameConference on Graphics, Patterns and Images, 24 (SIBGRAPI)
Conference LocationMaceió
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2011-07-23 15:36:12 :: -> administrator :: 2011
2020-02-19 02:19:27 :: administrator -> :: 2011
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Content TypeExternal Contribution
Tertiary TypeFull Paper
Keywords3D virtual replica, high-resolution texture mapping, least-squares minimization.
AbstractThis paper presents a texture projection and alignment method specifically tailored for objects with low geometric features. A common way to build virtual replicas is to acquire the geometry with a 3D scanner and model its reflectance properties (i.e. colors) by projecting photos onto the 3D surface. To correctly align each photo it is necessary to retrieve the camera's extrinsic matrix, which is usually achieved by optimization algorithms that match geometric features of the model with their corresponding ones on the photos. However, when dealing with cultural heritage artifacts or mechanical pieces, for example, some models inherently lack significant geometric detail. We propose a method to automatically extract these features from high resolution photos and register them in a sequential manner using a variation of the contour-based approaches. The algorithm requires minimum user intervention as only an initial guess of the camera's direction is required for each photo. In addition, we describe an improved formulation of the optimization method based on a smooth function that avoids typical local minima found in this scenario. The results are illustrated with high-resolution textured models produced by our system.
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