DocumentCode
3512351
Title
Automatic Texture Acquisition for 3D Model Using Oblique Aerial Images
Author
Wang, Mi ; Bai, Hao ; Hu, Fen
Author_Institution
State Key Lab. of Inf. Eng. in Surveying, Wuhan Univ., Wuhan
fYear
2008
fDate
1-3 Nov. 2008
Firstpage
495
Lastpage
498
Abstract
This paper describes an approach for automatic texture acquisition obtained from 3D city model and oblique aerial images. Firstly, the image feature lines should be extracted. Secondly, the extracted lines are matched with the corresponding 3D feature lines of object-space from the 3D city model, by estimating the image-space position of the 3D lines with the coarsely provided exterior orientation (EO) parameters of the image. Then, for each corresponding lines, we could list two equations based on the coplanarity constraints of object-space; that is, the accurate EO parameters can be calculated by least square method (LSM) with at least three pairs of conjugate lines. Finally, the interest image areas including the building surfaces are rectified with the refined EO parameters and the textures are obtained. In this paper, the principles of the method are described in detail along with an experiment carried out on a dataset of downtown area abroad, which has proved the correctness and robustness of such method.
Keywords
feature extraction; geographic information systems; image texture; 3D city model; 3D feature lines; automatic texture acquisition; coplanarity constraints; exterior orientation parameters; image feature line extraction; image space position; least square method; object space; oblique aerial images; Cities and towns; Data mining; Equations; Feature extraction; Geographic Information Systems; Image edge detection; Intelligent networks; Intelligent systems; Laboratories; Remote sensing; 3D Model; Oblique Aerial Images; Texture Acquisition;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Networks and Intelligent Systems, 2008. ICINIS '08. First International Conference on
Conference_Location
Wuhan
Print_ISBN
978-0-7695-3391-9
Electronic_ISBN
978-0-7695-3391-9
Type
conf
DOI
10.1109/ICINIS.2008.122
Filename
4683272
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