Full Camera Calibration from a Single View of Planar Scene
We present a novel algorithm that applies conics to realize reliable camera calibration. In particular, we show that a single view of two coplanar circles is sufficiently powerful to give a fully automatic calibration framework that estimates both intrinsic and extrinsic parameters. This method stems from the previous work of conic based calibration and calibration-free scene analysis. It eliminates many a priori constraints such as known principal point, restrictive calibration patterns, or multiple views. Calibration is achieved statistically through identifying multiple orthogonal directions and optimizing a probability function by maximum likelihood estimate. Orthogonal vanishing points, which build the basic geometric primitives used in calibration, are identified based on the fact that they represent conjugate directions with respect to an arbitrary circle under perspective transformation. Experimental results from synthetic and real scenes demonstrate the effectiveness, accuracy, and popularity of the approach.
KeywordsAugmented Reality Camera Calibration Intrinsic Parameter Principal Point Single View
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