Neural Tree Reconstruction for the Open Forest Observatory

2026-06-16

AI Summary

This work addresses the limitations of traditional Structure-from-Motion (SfM)-based 3D forest reconstruction, which often suffers from artifacts and missing details in understory regions due to occlusion and sparse viewing angles, thereby compromising accuracy in ecological and climate applications. For the first time, we integrate Neural Radiance Fields (NeRF) into a large-scale canopy reconstruction pipeline at an open forest observatory, combining drone imagery, geospatial data, and open-source 3D toolchains to produce high-fidelity forest structural models. Our approach significantly enhances reconstruction robustness and fine-detail recovery under challenging conditions of severe occlusion and limited viewpoints, effectively reducing understory errors. The resulting models provide more reliable foundational data for downstream tasks such as carbon stock monitoring and wildfire simulation, fostering deeper integration between data-driven priors and ecological modeling.

Abstract

The Open Forest Observatory (OFO) is a collaboration across universities and other partners to make low-cost forest mapping accessible to ecologists, land managers, and the general public. The OFO is building both a database of geospatial forest data as well as open-source methods and tools for forest mapping by uncrewed aerial vehicle. Such data are useful for a variety of climate applications including prioritizing reforestation efforts, informing wildfire hazard reduction, and monitoring carbon sequestration. In the current iteration of the OFO's forest map database, 3D tree maps are created using classical structure-from-motion techniques. This approach is prone to artifacts, lacks detail, and has particular difficulty on the forest floor where the input data (overhead imagery) has limited visibility. These reconstruction errors can potentially propagate to the downstream scientific tasks (e.g. a wildfire simulation.) Advances in 3D reconstruction, including methods like Neural Radiance Fields (NeRF), produce higher quality results that are more robust to sparse views and support data-driven priors. We explore ways to incorporate NeRFs into the OFO dataset, outline future work to support even more state-of-the-art 3D vision models, and describe the importance of high-quality 3D reconstructions for forestry applications.

Problem

• 3D reconstruction
• forest mapping
• structure-from-motion
• Neural Radiance Fields
• reconstruction artifacts

Innovation

• Neural Radiance Fields
• 3D reconstruction
• forest mapping
• structure-from-motion
• uncrewed aerial vehicle