A Novel Solution for Drone Photogrammetry with Low-overlap Aerial Images using Monocular Depth Estimation

• URL: http://arxiv.org/abs/2503.04513v1
• Date: Thu, 06 Mar 2025 14:59:38 GMT
• Title: A Novel Solution for Drone Photogrammetry with Low-overlap Aerial Images using Monocular Depth Estimation
• Authors: Jiageng Zhong, Qi Zhou, Ming Li, Armin Gruen, Xuan Liao,
• Abstract summary: Low-overlap aerial imagery poses significant challenges to traditional photogrammetric methods.<n>We propose a novel workflow based on monocular depth estimation to address the limitations of conventional techniques.
• Score: 6.689484367905018
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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