A Novel Solution for Drone Photogrammetry with Low-overlap Aerial Images using Monocular Depth Estimation
This addresses challenges in drone mapping for applications like surveying or environmental monitoring, but it is incremental as it builds on existing monocular depth techniques.
The study tackled the problem of low-overlap aerial imagery in drone photogrammetry by using monocular depth estimation to generate dense depth information, achieving meter-level accuracy and significantly better completeness compared to traditional methods.
Low-overlap aerial imagery poses significant challenges to traditional photogrammetric methods, which rely heavily on high image overlap to produce accurate and complete mapping products. In this study, we propose a novel workflow based on monocular depth estimation to address the limitations of conventional techniques. Our method leverages tie points obtained from aerial triangulation to establish a relationship between monocular depth and metric depth, thus transforming the original depth map into a metric depth map, enabling the generation of dense depth information and the comprehensive reconstruction of the scene. For the experiments, a high-overlap drone dataset containing 296 images is processed using Metashape to generate depth maps and DSMs as ground truth. Subsequently, we create a low-overlap dataset by selecting 20 images for experimental evaluation. Results demonstrate that while the recovered depth maps and resulting DSMs achieve meter-level accuracy, they provide significantly better completeness compared to traditional methods, particularly in regions covered by single images. This study showcases the potential of monocular depth estimation in low-overlap aerial photogrammetry.