3DWPGS: End-to-end 3D Gaussian splatting for woody-plant modeling and physical simulation

The 3D reconstruction and physical simulation of plants in natural scenes are of significant research and practical value in fields such as agronomy, forestry, ecology, and remote sensing. However, mainstream 3D reconstruction methods generally focus on geometric detail recovery but lack integration with physics-driven approaches, making it challenging to accurately and efficiently simulate the dynamic changes in plant structures. Although the latest physical Gaussian methods can simulate a variety of nonrigid deformations, there is limited consideration of plant-specific structural features, which affects the accuracy of reconstruction and simulation. To address this challenge, an end-to-end woody-plant Gaussian is proposed, which is a framework of high-precision 3D reconstruction and physical simulation for woody plants. This framework begins by fine-tuning a pre-trained plant instance segmentation model tailored for this purpose to reduce environmental noise interference and improve the accuracy of skeleton extraction from point cloud data. It leverages the extracted topology to guide fine-grained hierarchical classification of branches. By segmenting hierarchical radii and cross-sectional proportions, the Gaussian point distribution is constrained, enabling the Gaussian ellipsoids to better align with branch surfaces, thereby enhancing reconstruction details. In the physical simulation stage, the framework incorporates material property variation rules. Using topological guidance, Gaussian ellipsoids are mapped to branch hierarchies, and a cantilever beam physical model predicts Gaussian distributions and covariance matrix parameters. This approach not only improves rendering quality but also enhances the realism of branch-bending simulations. Finally, we evaluate our framework on the photos of real woody plants we took (3D deformable wood plant) and a public dataset (NeRF-synthetic). Compared to existing plant reconstruction methods, woody-plant Gaussian achieves state-of-the-art performance and significantly improves the visual quality of plant physical simulations.