TR2013-112

Extrinsic Camera Calibration Without a Direct View Using Spherical Mirror


    •  Agrawal, A., "Extrinsic Camera Calibration Without a Direct View Using Spherical Mirror", IEEE International Conference on Computer Vision (ICCV), December 2013.
      BibTeX TR2013-112 PDF
      • @inproceedings{Agrawal2013dec,
      • author = {Agrawal, A.},
      • title = {Extrinsic Camera Calibration Without a Direct View Using Spherical Mirror},
      • booktitle = {IEEE International Conference on Computer Vision (ICCV)},
      • year = 2013,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2013-112}
      • }
Abstract:

We consider the problem of estimating the extrinsic parameters (pose) of a camera with respect to a reference 3D object without a direct view. Since the camera does not view the object directly, previous approaches have utilized reflections in a planar mirror to solve this problem. However, a planar mirror based approach requires a minimum of three reflections and has degenerate configurations where estimation fails. In this paper, we show that the pose can be obtained using a single reflection in a spherical mirror of known radius. This makes our approach simpler and easier in practice. In addition, unlike planar mirrors, the spherical mirror based approach does not have any degenerate configurations, leading to a robust algorithm. While a planar mirror reflection results in a virtual perspective camera, a spherical mirror reflection results in a non-perspective axial camera. The axial nature of rays allows us to compute the axis (direction of sphere center) and few pose parameters in a linear fashion. We then derive an analytical solution to obtain the distance to the sphere center and remaining pose parameters and show that it corresponds to solving a 16th degree equation. We present comparisons with state-of-art method using planar mirrors and show that our approach recovers more accurate pose in the presence of noise. Extensive simulations and results on real data validate our algorithm.