用机载激光雷达数据生成地基点云，补全森林三维结构细节。
From Canopy to Ground via ForestGen3D: Learning Cross-Domain Generation of 3D Forest Structure from Aerial-to-Terrestrial LiDAR
- 基于条件去噪扩散模型，融合机载与地基激光雷达数据训练
- 生成的点云与机载数据空间一致，还原树高、胸径等关键参数
- 提出新评估指标EPC，适用于无真实地面数据场景
生态系统中生物与非生物组分的三维结构对生态过程及自然和人为干扰的反馈至关重要。预测野火、干旱、病害或大气沉降的影响依赖于精确的三维植被结构表征，但广泛测量成本高昂且常不可行。我们提出ForestGen3D，一种跨域生成框架，可在保留机载激光雷达（ALS）观测的三维森林结构基础上，推断缺失的林下细节。该模型基于共注册的ALS与地基激光雷达（TLS）数据训练，采用条件去噪扩散概率模型，生成与ALS几何空间一致的真实感TLS类点云，实现全垂直森林结构的景观尺度重建。我们在混合针叶林生态系统的真实数据上，从树、样方到景观尺度进行评估，通过定性和定量的几何与分布分析表明，其生成结果在三维结构相似性及下游生物物理指标（如树高、胸径、冠幅、冠体积）上与TLS参考数据高度匹配。我们进一步引入并验证了期望点包含（EPC）指标，作为在缺乏TLS真值时生成质量的实用代理。结果表明，ForestGen3D在仅含ALS数据的环境中，通过推断生态合理的林下结构，忠实保留了ALS所编码的景观异质性，为生态分析、结构燃料表征及相关遥感应用提供了更丰富的三维表征。
原文摘要 · Abstract (English)
The 3D structure of living and non-living components in ecosystems plays a critical role in determining ecological processes and feedbacks from both natural and human-driven disturbances. Anticipating the effects of wildfire, drought, disease, or atmospheric deposition depends on accurate characterization of 3D vegetation structure, yet widespread measurement remains prohibitively expensive and often infeasible. We present ForestGen3D, a cross-domain generative framework that preserves aerial LiDAR (ALS) observed 3D forest structure while inferring missing sub-canopy detail. ForestGen3D is based on conditional denoising diffusion probabilistic models trained on co-registered ALS and terrestrial LiDAR (TLS) data. The model generates realistic TLS-like point clouds that remain spatially consistent with ALS geometry, enabling landscape-scalable reconstruction of full vertical forest structure. We evaluate ForestGen3D at tree, plot, and landscape scales using real-world data from mixed conifer ecosystems, and show through qualitative and quantitative geometric and distributional analyses that it produces high-fidelity reconstructions closely matching TLS reference data in terms of 3D structural similarity and downstream biophysical metrics, including tree height, DBH, crown diameter, and crown volume. We further introduce and demonstrate the expected point containment (EPC) metric which serves as a practical proxy for generation quality in settings where TLS ground truth is unavailable. Our results demonstrate that ForestGen3D enhances the utility of ALS only environments by inferring ecologically plausible sub-canopy structure while faithfully preserving the landscape heterogeneity encoded in ALS observations, thereby providing a richer 3D representation for ecological analysis, structural fuel characterization and related remote sensing applications.
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