Camera Calibration through Camera Projection Loss
- URL: http://arxiv.org/abs/2110.03479v1
- Date: Thu, 7 Oct 2021 14:03:10 GMT
- Title: Camera Calibration through Camera Projection Loss
- Authors: Talha Hanif Butt and Murtaza Taj
- Abstract summary: We propose a novel method to predict intrinsic (focal length and principal point offset) parameters using an image pair.
Unlike existing methods, we proposed a new representation that incorporates camera model equations as a neural network in multi-task learning framework.
Our proposed approach achieves better performance with respect to both deep learning-based and traditional methods on 7 out of 10 parameters evaluated.
- Score: 4.36572039512405
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 \emph{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
7 out of 10 parameters evaluated using both synthetic and real data. Our code
and generated dataset will be made publicly available to facilitate future
research.
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