SECPNet—secondary encoding network for estimating camera parameters

Abstract

Camera parameter estimation can be used in visual odometry, robot vision, SLAM, 3D reconstruction and other directions. It is also the main research content of computer vision. Based on the deep learning strategy, we propose a secondary encoding network for camera parameters (SECPNet), which can predict the camera parameters and recover the camera pose according to a single RGB image. Based on the three-dimensional dataset ShapeNet40 (Chang et al. in An information-rich 3D model repository, 2015. arXiv:1512.03012 ), we build a varifocal multi-viewpoint image dataset for camera parameter estimation. Experimental results show that our method has state-of-the-art performance in camera parameter estimation.