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.

相机参数估计可用于视觉里程计，机器人视觉，SLAM，3D重建和其他方向。它也是计算机视觉的主要研究内容。基于深度学习策略，我们提出了用于相机参数的辅助编码网络（SECPNet），该网络可以预测相机参数并根据单个RGB图像恢复相机姿态。基于三维数据集ShapeNet40（Chang等人，在一个信息丰富的3D模型资源库中，2015.arXiv：1512.03012），我们构建了用于相机参数估计的多焦点多视点图像数据集。实验结果表明，我们的方法在相机参数估计方面具有最先进的性能。