  For a number of tasks, such as 3D reconstruction, robotic interface,
autonomous driving, etc., camera calibration is essential. In this study, we
present a unique method for predicting intrinsic (principal point offset and
focal length) and extrinsic (baseline, pitch, and translation) properties from
a pair of images. We suggested a novel method where camera model equations are
represented as a neural network in a multi-task learning framework, in contrast
to existing methods, which build a comprehensive solution. By reconstructing
the 3D points using a camera model neural network and then using the loss in
reconstruction to obtain the camera specifications, this innovative camera
projection loss (CPL) method allows us that the desired parameters should be
estimated. As far as we are aware, our approach is the first one that uses an
approach to multi-task learning that includes mathematical formulas in a
framework for learning to estimate camera parameters to predict both the
extrinsic and intrinsic parameters jointly. Additionally, we provided a new
dataset named as CVGL Camera Calibration Dataset [1] which has been collected
using the CARLA Simulator [2]. Actually, we show that our suggested strategy
out performs both conventional methods and methods based on deep learning on 6
out of 10 parameters that were assessed using both real and synthetic data. Our
code and generated dataset are available at
https://github.com/thanif/Camera-Calibration-through-Camera-Projection-Loss.
