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In this paper, we study linear approaches for 3D model acquisition from non-calibrated images. First, the intrinsic andextrinsic camera calibration is taken into consideration. In particular, we study the use of a specific calibrationprimitive: the parallelepiped. Parallelepipeds are frequently present in man-made environments and naturally encode theaffine structure of the scene. Any information about their euclidean structure (angles or ratios of edge lengths), possiblycombined with information about camera parameters is useful to obtain the euclidean reconstruction. We propose anelegant formalism to incorporate such information, in which camera parameters are dual to parallelepiped parameters,i.e. any knowledge about one entity provides constraints on the parameters of the others. Consequently, an image aparallelepiped with known Euclidean structure allows to compute the intrinsic camera parameters, and reciprocally, acalibrated image of a parallelepiped allows to recover its euclidean shape (up to size). On the conceptual level, thisduality can be seen as an alternative way to understand camera calibration: usually, calibration is considered to beequivalent to localizing the absolute conic or quadric in an image, whereas here we show that other primitives, such ascanonic parallelepipeds, can be used as well. While the main contributions of this work concern the estimation ofcamera and parallelepiped parameters. The complete system allows both calibration and 3D model acquisition from asmall number of arbitrary images with a reasonable amount of user interaction. |
