IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Panoramic representation for route recognition by a mobile robot
International Journal of Computer Vision - Special issue on machine vision research at Osaka University
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
QuickTime VR: an image-based approach to virtual environment navigation
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Plenoptic modeling: an image-based rendering system
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Rendering with concentric mosaics
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Computer Vision: Three-Dimensional Data from Images
Computer Vision: Three-Dimensional Data from Images
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This chapter proposes a novel approach for the calibration of a panoramic camera using geometric information available in real scenes. Panoramic cameras are of increasing importance for various applications in computer vision, computer graphics or robotics. Previously developed camera calibration methods (for 'standard' camera architectures following the pinhole camera model) are not applicable due to the non-linearity of the panoramic camera, defined by the existence of multiple (nonlinear) optical centers and a cylindrical image manifold. This article addresses the calibration subject of a more general yet flexible panoramic camera model for the first time. The chapter focuses on the calibration of two dominant parameters that characterize the camera model and provide flexibility in selecting different types of panoramas such as single-center (e.g. as assumed for QTVR), symmetric stereo, concentric or polycentric panoramas. We elaborate selected geometric constraints (for increasing numerical stability) with the corresponding solutions; summarize the experimental results with captured image data, and discuss the performance of different geometric constraints via error-sensitivity simulation and analysis.