  This paper considers outdoor terrain mapping using RGB images obtained from
an aerial vehicle. While feature-based localization and mapping techniques
deliver real-time vehicle odometry and sparse keypoint depth reconstruction, a
dense model of the environment geometry and semantics (vegetation, buildings,
etc.) is usually recovered offline with significant computation and storage.
This paper develops a joint 2D-3D learning approach to reconstruct a local
metric-semantic mesh at each camera keyframe maintained by a visual odometry
algorithm. Given the estimated camera trajectory, the local meshes can be
assembled into a global environment model to capture the terrain topology and
semantics during online operation. A local mesh is reconstructed using an
initialization and refinement stage. In the initialization stage, we estimate
the mesh vertex elevation by solving a least squares problem relating the
vertex barycentric coordinates to the sparse keypoint depth measurements. In
the refinement stage, we associate 2D image and semantic features with the 3D
mesh vertices using camera projection and apply graph convolution to refine the
mesh vertex spatial coordinates and semantic features based on joint 2D and 3D
supervision. Quantitative and qualitative evaluation using real aerial images
show the potential of our method to support environmental monitoring and
surveillance applications.
