  Camera calibration is a necessity in various tasks including 3D
reconstruction, hand-eye coordination for a robotic interaction, autonomous
driving, etc. In this work we propose a novel method to predict extrinsic
(baseline, pitch, and translation), intrinsic (focal length and principal point
offset) parameters using an image pair. Unlike existing methods, instead of
designing an end-to-end solution, we proposed a new representation that
incorporates camera model equations as a neural network in multi-task learning
framework. We estimate the desired parameters via novel camera projection loss
(CPL) that uses the camera model neural network to reconstruct the 3D points
and uses the reconstruction loss to estimate the camera parameters. To the best
of our knowledge, ours is the first method to jointly estimate both the
intrinsic and extrinsic parameters via a multi-task learning methodology that
combines analytical equations in learning framework for the estimation of
camera parameters. We also proposed a novel dataset using CARLA Simulator.
Empirically, we demonstrate that our proposed approach achieves better
performance with respect to both deep learning-based and traditional methods on
8 out of 10 parameters evaluated using both synthetic and real data. Our code
and generated dataset are available at
https://github.com/thanif/Camera-Calibration-through-Camera-Projection-Loss.
