  This paper addresses outdoor terrain mapping using overhead images obtained
from an unmanned aerial vehicle. Dense depth estimation from aerial images
during flight is challenging. While feature-based localization and mapping
techniques can deliver real-time odometry and sparse points reconstruction, a
dense environment model is generally recovered offline with significant
computation and storage. This paper develops a joint 2D-3D learning approach to
reconstruct local meshes at each camera keyframe, which can be assembled into a
global environment model. Each local mesh is initialized from sparse depth
measurements. We associate image features with the mesh vertices through camera
projection and apply graph convolution to refine the mesh vertices based on
joint 2-D reprojected depth and 3-D mesh supervision. Quantitative and
qualitative evaluations using real aerial images show the potential of our
method to support environmental monitoring and surveillance applications.
