IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Recovering range using virtual multicamera stereo
Computer Vision and Image Understanding
QuickTime VR: an image-based approach to virtual environment navigation
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
3-D Scene Data Recovery Using Omnidirectional Multibaseline Stereo
International Journal of Computer Vision
Rendering with concentric mosaics
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Stereo Reconstruction from Multiperspective Panoramas
IEEE Transactions on Pattern Analysis and Machine Intelligence
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Camera Models and Fundamental Concepts Used in Geometric Computer Vision
Foundations and Trends® in Computer Graphics and Vision
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Cylindrical panoramas can be classified into various types according to their basic scanning properties and mutual spatial alignment, such as single-center (e.g., as in QTVR), concentric, multi-center, symmetric, or (after a transformation onto a cylinder) catadioptric panoramas. This paper deals with a solution of the sensor pose estimation problem using (somehow calculated) corresponding points in the multi-center panoramas. All other types of panoramas are able to be described by this general multi-center model. Due to the non-linearity of the multi-centered projection geometry, the modeling of sensor pose estimation typically results into non-linear and highly complicated forms which incur numerical instability. This paper shows that there exist linear models for sensor pose estimation under minor geometrical constraints, namely symmetric and leveled panoramas. The presented approaches are important for solving the 3D data fusion problem for multiple panoramas; it is also fundamental for an in-depth analysis of multi-view geometry of panoramic images.