TreeNet3D : A large scale tree benchmark for 3D tree modeling, carbon storage estimation and tree segmentation

Abstract

This paper presents a novel fully automated approach for generating structured 3D synthetic tree models, addressing the limitations of existing datasets used in applications like digital twin construction, carbon stock calculation, and environmental assessments. The method allows for the automated creation of a large-scale dataset containing 13,000 tree models of ten common species, each featuring a detailed 3D point cloud with hierarchical structures, precise parameters, and separate branch and leaf information. The dataset includes both original and noise-added point clouds to enhance method testing and evaluation. It stands out by providing comprehensive structural data, including branch numbering and detailed tree skeleton information with node hierarchies and radius data. Furthermore, this study introduces randomly distributed batches of tree models within specific terrains. It provides results from airborne laser scanning simulations, which facilitate the individualized segmentation of these tree models. This first-of-its-kind, extensive synthetic dataset is designed for accurate algorithm evaluation in tasks such as branch-leaf separation, 3D reconstruction, individual tree segmentation and carbon stock estimation. The paper validates the dataset’s utility by applying state-of-the-art algorithms to demonstrate its effectiveness in various applications, marking a significant advancement in 3D tree modeling research. The datasets are publicly available, accessible via the ‘‘TreeNet3D Dataset’’ link.

Keywords

• 3D tree modeling
• Synthetic data
• Point cloud
• Segmentation
• Skeleton extraction