  The most prevalent routine for camera calibration is based on the detection
of well-defined feature points on a purpose-made calibration artifact. These
could be checkerboard saddle points, circles, rings or triangles, often printed
on a planar structure. The feature points are first detected and then used in a
nonlinear optimization to estimate the internal camera parameters.We propose a
new method for camera calibration using the principle of inverse rendering.
Instead of relying solely on detected feature points, we use an estimate of the
internal parameters and the pose of the calibration object to implicitly render
a non-photorealistic equivalent of the optical features. This enables us to
compute pixel-wise differences in the image domain without interpolation
artifacts. We can then improve our estimate of the internal parameters by
minimizing pixel-wise least-squares differences. In this way, our model
optimizes a meaningful metric in the image space assuming normally distributed
noise characteristic for camera sensors.We demonstrate using synthetic and real
camera images that our method improves the accuracy of estimated camera
parameters as compared with current state-of-the-art calibration routines. Our
method also estimates these parameters more robustly in the presence of noise
and in situations where the number of calibration images is limited.

Computer Vision and Pattern Recognition