DocumentCode :
2778489
Title :
Creating 3D models with uncalibrated cameras
Author :
Han, Mei ; Kanade, Takeo
Author_Institution :
Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA
fYear :
2000
fDate :
2000
Firstpage :
178
Lastpage :
185
Abstract :
We describe a factorization-based method to recover 3D models from multiple perspective views with uncalibrated cameras. The method first performs a projective reconstruction using a bilinear factorization algorithm, and then converts the projective solution to a Euclidean one by enforcing metric constraints. We present three factorization-based normalization algorithms to generate the Euclidean reconstruction and the intrinsic parameters, assuming zero skews. The first two algorithms are linear, one for dealing with the case that only the focal lengths are unknown, and another for the case that the focal lengths and the constant principal point are unknown. The third algorithm is bilinear dealing with the case that the focal lengths, the principal points and the aspect ratios are all unknown. We present the results of applying this method to building modeling, terrain recovery and multi-camera calibration
Keywords :
image reconstruction; terrain mapping; 3D models; Euclidean reconstruction; bilinear factorization; building modeling; intrinsic parameters; multi-camera calibration; multiple perspective views; projective reconstruction; terrain recovery; uncalibrated cameras; Calibration; Cameras; Image reconstruction; Image sequences; Iterative algorithms; Robot vision systems; Robustness; Shape; Singular value decomposition; Subspace constraints;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Applications of Computer Vision, 2000, Fifth IEEE Workshop on.
Conference_Location :
Palm Springs, CA
Print_ISBN :
0-7695-0813-8
Type :
conf
DOI :
10.1109/WACV.2000.895420
Filename :
895420
Link To Document :
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