用单目深度估计解决低重叠航拍图像的建模难题
A Novel Solution for Drone Photogrammetry with Low-overlap Aerial Images using Monocular Depth Estimation
- 通过航拍三角测量点建立单目深度与真实深度的关系
- 在20张低重叠图像上实现米级精度与更高完整性
- 适合无人机航拍数据稀疏场景的三维重建
低重叠航拍影像给传统摄影测量方法带来显著挑战，因其依赖高图像重叠度以生成精确完整的测绘成果。本研究提出一种基于单目深度估计的新流程，以克服传统方法局限。该方法利用航空三角测量获取的同名点，建立单目深度与实际地理深度之间的映射关系，将原始深度图转换为具有量纲的真实深度图，从而生成密集深度信息并实现场景完整重建。实验中，使用包含296张图像的高重叠无人机数据集，通过Metashape生成深度图和数字表面模型（DSM）作为真值；随后从中选取20张图像构建低重叠数据集进行评估。结果表明，尽管恢复的深度图与生成的DSM达到米级精度，但在仅由单张图像覆盖区域的完整性方面显著优于传统方法。本研究展示了单目深度估计在低重叠航拍摄影测量中的潜力。
原文摘要 · Abstract (English)
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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