用普通全景相机+算法，低成本精准测树木胸径。
Estimating the Diameter at Breast Height of Trees in a Forest from RGB
- 用照片重建3D点云，再用AI分割树干，最后用几何算法估算胸径。
- 在43棵树上误差仅5-9%，比激光雷达高2-4%但成本低得多。
- 适合林业调查、碳汇监测等需要快速测量的场景。
森林资源调查依赖于胸径（DBH）的准确测量，用于生态监测、资源管理与碳核算。虽然基于激光雷达的技术可达到厘米级精度，但成本高昂且操作复杂。本文提出一种低成本替代方案，仅需消费级360°视频相机。半自动化流程包括：(i) 使用Agisoft Metashape进行结构光摄影测量生成密集点云；(ii) 将Grounded SAM掩码投影至3D点云实现树干语义分割；(iii) 采用鲁棒的RANSAC方法估计横截面形状与DBH。我们还开发了交互式可视化工具，用于检查分割结果与估测值。在43棵树、61次采集数据下，该方法中位绝对相对误差为5-9%，仅比激光雷达结果高出2-4%，而仅使用一台成本低数个数量级的360相机，部署简便且设备普及率高。
原文摘要 · Abstract (English)
Forest inventories rely on accurate measurements of the diameter at breast height (DBH) for ecological monitoring, resource management, and carbon accounting. While LiDAR-based techniques can achieve centimeter-level precision, they are cost-prohibitive and operationally complex. We present a low-cost alternative that only needs a consumer-grade 360 video camera. Our semi-automated pipeline comprises of (i) a dense point cloud reconstruction using Structure from Motion (SfM) photogrammetry software called Agisoft Metashape, (ii) semantic trunk segmentation by projecting Grounded Segment Anything (SAM) masks onto the 3D cloud, and (iii) a robust RANSAC-based technique to estimate cross section shape and DBH. We introduce an interactive visualization tool for inspecting segmented trees and their estimated DBH. On 61 acquisitions of 43 trees under a variety of conditions, our method attains median absolute relative errors of 5-9% with respect to "ground-truth" manual measurements. This is only 2-4% higher than LiDAR-based estimates, while employing a single 360 camera that costs orders of magnitude less, requires minimal setup, and is widely available.
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