Inferring Camera Intrinsics Based on Surfaces of Revolution: A Single Image Geometric Network Approach for Camera Calibration

Camera calibration is a necessary prerequisite in many applications of robotics, especially in robot vision in order to obtain metric reconstruction from a 2D image. In this paper, we address the problem of calibrating from a single image of a surface of revolution (SOR) based on deep learning, in order to determine the camera intrinsic parameters. Geometric constraints based on the symmetry properties of the SOR structure are deployed to our proposed learning-based camera calibration framework. To enable the calibration from a single view, we also propose a learning-based conics detection model fitting the geometric primitive of a cylinder. The calibration from a single view can be completed by minimizing the geometric constraints of two conics detected by the learning-based model with cylinder images as input. Objects with a surface of revolution are commonly visible in daily life, such as cans, bottles, and bowls, making this research both significant and practical. Finally, traditional calibration techniques are compared against our single image calibration. Experiments conducted on newly collected datasets demonstrate the effectiveness and robustness of the proposed method.