Reconstruction of Linearly Parameterized Models from a Single Image Using the Vanishing Points
We present a method using only three vanishing points to recover the dimensions of object and its pose from single image of perspective projection with a camera of unknown focal length. Our approach is to compute the dimensions of objects represented by the unit vector of objects from the image. The dimension vector v of objects can be solved by the standard nonlinear optimization techniques with a multistart method which generates multiple starting points for the optimizer by sampling the parameter space uniformly. This method allows model-based vision to be computed the dimensions of object for a 3D model from matches to a single 2D image. Experimental results demonstrate the dimension vector v of the proposed method from a single image using three vanishing points and show a performance of the proposed method compared to the conventional. Then, the actual dimensions of object from the image agree well with the calculated results.
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