Camera Self Calibration refers to the process by which a camera system autonomously estimates and refines its intrinsic parameters without relying on external calibration objects or known 3D structures. The intrinsic parameters of a camera include characteristics such as focal length, principal point, and lens distortion, which are crucial for accurate geometric reconstruction and image processing.


Degeneracy in Self-Calibration Revisited and a Deep Learning Solution for Uncalibrated SLAM

Self-calibration of camera intrinsics and radial distortion has a long history of research in the computer vision community. However, it remains rare to see real applications of such techniques to modern Simultaneous Localization And Mapping (SLAM) systems, especially in driving scenarios. In this paper, we revisit the geometric approach to this problem, and provide a theoretical proof that explicitly shows the ambiguity between radial distortion and scene depth when two-view geometry is used to self-calibrate the radial distortion. In view of such geometric degeneracy, we propose a learning approach that trains a convolutional neural network (CNN) on a large amount of synthetic data. We demonstrate the utility of our proposed method by applying it as a checkerboard-free calibration tool for SLAM, achieving comparable or superior performance to previous learning and hand-crafted method