# From Canopy to Ground via ForestGen3D: Learning Cross-Domain Generation of 3D Forest Structure from Aerial-to-Terrestrial LiDAR

arXiv: https://arxiv.org/abs/2509.16346 · HTML: https://mlsanity.com/paper/2509.16346/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling](https://mlsanity.com/paper/1610.07584/) (2016)
- [PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows](https://mlsanity.com/paper/1906.12320/) (2019)
- [PCN: Point Completion Network](https://mlsanity.com/paper/1808.00671/) (2018)
- [Statistical Aspects of Wasserstein Distances](https://mlsanity.com/paper/1806.05500/) (2018)
- [A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion](https://mlsanity.com/paper/2112.03530/) (2021)
- [Forest Tree Detection and Segmentation using High Resolution Airborne LiDAR](https://mlsanity.com/paper/1810.12536/) (2018)
- [ForestAlign: Automatic Forest Structure-based Alignment for Multi-view TLS and ALS Point Clouds](https://mlsanity.com/paper/2302.12989/) (2023)

## Abstract (arXiv)

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.