MAKE OBJECT CONNECT: A POSE ESTIMATION NETWORK FOR UAV IMAGES OF THE OUTDOOR SCENE

As the basics of 3D vision, pose estimation with 2D images is of significance in 3D reconstruction, UAV positioning, and other fields. However, the related works focus on the natural images and pay less attention to the wide-coverage UAV remote sensing (RS) images. In fact, the relationship between objects in UAV images can benefit pose estimation. Therefore, aiming at the outdoor scene captured by the UAV monocular camera, a novel pose estimation network that emphasizes the association between objects is proposed. The multi-scale visual features extracted by the convolutional neural network (CNN) are manipulated by the object-agnostic segmentation model to indicate the existing space of all possible objects in the whole scene. The features of all possible objects are embedded into vectors, and then processed with a graph convolution network (GCN) for relationship analysis. Based on the known sparse point cloud and the optimized features of 2D images, the camera pose is regressed iteratively by 3D visual geometry. To verify the feasibility of the network, experiments are conducted on the Extended CMU Seasons and the simulation UAV dataset. Results prove that our network emphasizes more features on the small objects and obtains superior pose estimation results.