Aerial remote sensing enables efficient large-area surveying, but accurate direct object-level measurement remains difficult in complex natural scenes. Recent advancements in 3D vision, particularly learned radiance-field representations such as NeRF and 3D Gaussian Splatting, have begun to raise the ceiling on reconstruction fidelity and densifiable geometry from posed imagery. Nevertheless, direct aerial measurement of important natural attributes such as tree diameter at breast height (DBH) remains challenging. Trunks in aerial forest scans are distant and sparsely observed in image views: at typical operating altitudes, stems may span only a few pixels. With these constraints, conventional reconstruction methods leave breast-height trunk geometry weakly constrained. We present TreeDGS, an aerial image reconstruction method that leverages 3D Gaussian Splatting as a continuous, densifiable scene representation for trunk measurement. After SfM--MVS initialization and Gaussian optimization, we extract a dense point set from the Gaussian field using RaDe-GS's depth-aware cumulative-opacity integration and associate each sample with a multi-view opacity reliability score. Then, we estimate DBH from trunk-isolated points using opacity-weighted solid-circle fitting. Evaluated on 10 plots with field-measured DBH, TreeDGS reaches 4.79,cm RMSE (about 2.6 pixels at this GSD) and outperforms a state-of-the-art LiDAR baseline (7.91,cm RMSE). This shows that TreeDGS can enable accurate, low-cost aerial DBH measurement

翻译：航空遥感能够实现高效的大面积勘测，但在复杂的自然场景中，精确的直接物体级测量仍然困难。三维视觉的最新进展，特别是诸如NeRF和3D高斯泼溅等学习的辐射场表示方法，已经开始提升基于姿态图像的重建保真度和可致密几何的上限。然而，对于树木胸径等重要自然属性的直接航空测量仍然具有挑战性。航空森林扫描中的树干距离遥远且在图像视图中观测稀疏：在典型的作业高度下，树干可能仅跨越几个像素。在这些限制下，传统的重建方法使得胸高处的树干几何约束较弱。我们提出了TreeDGS，一种利用3D高斯泼溅作为连续、可致密的场景表示来进行树干测量的航空图像重建方法。在SfM–MVS初始化和高斯优化之后，我们使用RaDe-GS的深度感知累积不透明度积分从高斯场中提取密集点集，并将每个样本与一个多视角不透明度可靠性分数相关联。然后，我们使用不透明度加权的实心圆拟合，从树干分离的点中估计胸径。在10个具有实地测量胸径的样地上进行评估，TreeDGS达到了4.79厘米的均方根误差（在此地面采样距离下约为2.6个像素），并优于最先进的激光雷达基线（7.91厘米均方根误差）。这表明TreeDGS能够实现精确、低成本的航空胸径测量。