TerrainMesh: Metric-Semantic Terrain Reconstruction from Aerial Images Using Joint 2D-3D Learning

TL;DR

This paper introduces TerrainMesh, a joint 2D-3D learning method for real-time, dense, metric-semantic terrain reconstruction from aerial images, enabling efficient online environment modeling for monitoring and surveillance.

Contribution

It presents a novel joint learning approach that reconstructs local meshes with semantic information using a two-stage process, integrating sparse depth and semantic features into a global terrain model.

Findings

Effective local mesh reconstruction with semantic labels.

Supports real-time terrain mapping during online operation.

Demonstrated potential for environmental monitoring applications.

Abstract

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.