2D-3Dジョイント学習による航空画像からのメッシュ再構成と屋外地形マッピング
Mesh Reconstruction from Aerial Images for Outdoor Terrain Mapping Using Joint 2D-3D Learning
ドローン撮影の航空画像から、2D再投影深度と3Dメッシュのジョイント学習で局所メッシュを再構成し、それらを統合して屋外地形の密な環境モデルを構築する手法を提案した。
著者: Qiaojun Feng, Nikolay Atanasov
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
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.
