用NeRF技术加速红杉林树干直径估算，精度超传统方法。
NeRF-Accelerated Ecological Monitoring in Mixed-Evergreen Redwood Forest
- 结合移动激光扫描与神经辐射场重建森林
- 新方法误差仅1.68厘米，优于标准圆柱建模
- 适合生态监测、林木碳汇研究者使用
森林测绘为理解森林动态提供了关键观测数据。树干直径（DBH）是估算森林生物量和二氧化碳固存的重要指标。传统人工测绘耗时费力，难以实现大规模应用；自动化方法依赖密集的森林三维重建，通常以点云形式呈现。地面激光扫描（TLS）和移动激光扫描（MLS）虽能生成高质量点云，但依赖昂贵的激光雷达设备。神经辐射场（NeRF）是一种新兴视觉重建技术，可通过少量输入视角训练神经网络实现逼真三维重建。本文对比了MLS与NeRF在混合常绿红杉林中用于树干直径估算的效果，并提出基于凸包建模的改进DBH估计方法。实验表明，该方法达到1.68厘米的均方根误差（RMSE），显著优于传统圆柱拟合方法。相关代码与数据集已开源：https://github.com/harelab-ucsc/RedwoodNeRF。
原文摘要 · Abstract (English)
Forest mapping provides critical observational data needed to understand the dynamics of forest environments. Notably, tree diameter at breast height (DBH) is a metric used to estimate forest biomass and carbon dioxide sequestration. Manual methods of forest mapping are labor intensive and time consuming, a bottleneck for large-scale mapping efforts. Automated mapping relies on acquiring dense forest reconstructions, typically in the form of point clouds. Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) generate point clouds using expensive LiDAR sensing, and have been used successfully to estimate tree diameter. Neural radiance fields (NeRFs) are an emergent technology enabling photorealistic, vision-based reconstruction by training a neural network on a sparse set of input views. In this paper, we present a comparison of MLS and NeRF forest reconstructions for the purpose of trunk diameter estimation in a mixed-evergreen Redwood forest. In addition, we propose an improved DBH-estimation method using convex-hull modeling. Using this approach, we achieved 1.68 cm RMSE, which consistently outperformed standard cylinder modeling approaches. Our code contributions and forest datasets are freely available at https://github.com/harelab-ucsc/RedwoodNeRF.
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