Advancing the Understanding of Fine-Grained 3D Forest Structures using Digital Cousins and Simulation-to-Reality: Methods and Datasets

TL;DR

The paper tackles the scarcity of large, annotated 3D forest data by introducing Boreal3D, a synthetic, multi-platform forest point cloud dataset generated via Digital Cousins and Sim2Real. It demonstrates that pretraining on Boreal3D followed by real-data fine-tuning—requiring as little as 20% real annotations—yields performance comparable to real-data-only models and significantly boosts cross-platform forest analysis. Through extensive experiments on semantic and instance segmentation across TLS, MLS, ULS, and ALS, the work shows synthetic data can meaningfully bridge the gap to real-world performance while providing error-free ground truth for structural attributes. Boreal3D thus emerges as a scalable resource for multi-task, multi-platform forest scene understanding with practical implications for ecological monitoring and management.

Abstract

Understanding and analyzing the spatial semantics and structure of forests is essential for accurate forest resource monitoring and ecosystem research. However, the lack of large-scale and annotated datasets has limited the widespread use of advanced intelligent techniques in this field. To address this challenge, a fully automated synthetic data generation and processing framework based on the concepts of Digital Cousins and Simulation-to-Reality (Sim2Real) is proposed, offering versatility and scalability to any size and platform. Using this process, we created the Boreal3D, the world's largest forest point cloud dataset. It includes 1000 highly realistic and structurally diverse forest plots across four different platforms, totaling 48,403 trees and over 35.3 billion points. Each point is labeled with semantic, instance, and viewpoint information, while each tree is described with structural parameters such as diameter, crown width, leaf area, and total volume. We designed and conducted extensive experiments to evaluate the potential of Boreal3D in advancing fine-grained 3D forest structure analysis in real-world applications. The results demonstrate that with certain strategies, models pre-trained on synthetic data can significantly improve performance when applied to real forest datasets. Especially, the findings reveal that fine-tuning with only 20% of real-world data enables the model to achieve performance comparable to models trained exclusively on entire real-world data, highlighting the value and potential of our proposed framework. The Boreal3D dataset, and more broadly, the synthetic data augmentation framework, is poised to become a critical resource for advancing research in large-scale 3D forest scene understanding and structural parameter estimation.

Figures (12)

• Figure 1: Overview of the workflow of dataset generation.
• Figure 2: The scan setups for each platform, from left to right: TLS, MLS, ULS and ALS. The black dots indicate the individual tree locations within a plot, while the red diamonds in TLS represent the five scanning locations. The dot lines in MLS depict the automatically planned moving trajectory, and the blue lines represent the tic-tac-toe trajectories for ULS. The green lines illustrate the flight trajectories for ALS.
• Figure 3: Top review of simulated multi-platform point clouds. From left to right: TLS, MLS, ULS and ALS. The red dots in TLS and MLS represent the scan locations and moving trajectories, respectively.
• Figure 4: Visualization of point cloud in a plot with semantic and instance annotations. (a) represents semantic annotations, and (b) represents instance annotations. Labels are colored randomly.
• Figure 5: The number of points for each semantic category in different platforms