Abstract

Camera calibration has been studied extensively in com- puter vision and photogrammetry, and the proposed techniques in the literature include those using 3D apparatus (two or three planes orthogonal to each other, or a plane undergoing a pure translation, etc.), 2D ob jects (planar patterns undergoing unknown motions), and 0D features (self-calibration using unknown scene points). This paper yet proposes a new calibration technique using 1D ob jects (points aligned on a line), thus filling the missing dimension in calibration. In particular, we show that camera calibration is not possible with free-moving 1D ob jects, but can be solved if one point is ?xed. A closed-form solution is developed if six or more observations of such a 1D ob ject are made. For higher accuracy, a nonlinear technique based on the maximum likelihood criterion is then used to re?ne the estimate. Besides the theoretical aspect, the proposed technique is also important in practice especially when calibrating multiple cameras mounted apart from each other, where the calibration ob jects are required to be visible simultaneously.