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CVPR 2013 Camera calibration

Single Image Calibration of Multi-Axial Imaging Systems

Amit Agrawal and Srikumar Ramalingam

Mitsubishi Electric Research Labs (MERL)

A reflective array of spherical mirrors on the left and a refractive array of transparent spheres on the right
A camera looking at spherical mirrors (left) and refractive spheres (right). Calibration of such systems requires estimating the sphere centers and radii in the camera coordinate system. Can we calibrate these imaging systems automatically using a single photo of a checkerboard?

Multiple spheres.
One calibration photograph.

Calibration and 3D reconstruction for multi-axial imaging systems. Our paper shows that calibration can be done using a single photo of a checkerboard.

Sphere geometry
Recover each sphere’s radius and location
Target pose
Estimate the unknown checkerboard pose
Flexible arrangements
No grid arrangement of spheres required
Automatic calibration
For an internally calibrated camera

Imaging systems consisting of a camera looking at multiple spherical mirrors (reflection) or multiple refractive spheres (refraction) have been used for wide-angle imaging applications. We describe such setups as multi-axial imaging systems, since a single sphere results in an axial system. Assuming an internally calibrated camera, calibration of such multi-axial systems involves estimating the sphere radii and locations in the camera coordinate system. However, previous calibration approaches require manual intervention or constrained setups. We present a fully automatic approach using a single photo of a 2D calibration grid. The pose of the calibration grid is assumed to be unknown and is also recovered. Our approach can handle unconstrained setups, where the mirrors/refractive balls can be arranged in any fashion, not necessarily on a grid.

The axial nature of rays allows us to compute the axis of each sphere separately. We then show that by choosing rays from two or more spheres, the unknown pose of the calibration grid can be obtained linearly and independently of sphere radii and locations. Knowing the pose, we derive analytical solutions for obtaining the sphere radius and location. This leads to an interesting result that 6-DOF pose estimation of a multi-axial camera can be done without the knowledge of full calibration. Simulations and real experiments demonstrate the applicability of our algorithm.