用几何流模拟植物4D生长，实现跨时间点的精准追踪。
Structure-aware Riemannian Growth Fields for 4D Plant Modeling
- 基于结构感知的黎曼流建模植物生长过程
- 在10天跨度上保持几何精度与对应一致性
- 适合植物形态学、生物建模研究者使用
本文提出一种新型4D植物生长建模框架，从稀疏的时间观测中重建植物连续的几何与拓扑演变。现有方法依赖密集配准，但受扫描限制和自遮挡影响，难以获取可靠密集序列，导致大时间间隔下快速器官生成破坏局部刚性时性能下降。为此，我们提出将植物形态发生建模为结构感知的黎曼生长场上的连续过程，联合建模拓扑变化与几何变形，保持植物层级结构与远距离时间点间的稳定时空对应。核心思想是将符号化生长规则嵌入连续测地线流中，使器官发育沿生物调节轨迹演化，维持拓扑变化下的结构一致性。我们还构建了一个包含10天、双物种、密集几何与语义标注的数据集。实验表明，该方法能准确追踪单个器官随时间生长，且在几何精度与对应一致性上显著优于当前最优基线。
原文摘要 · Abstract (English)
In this paper, we introduce a novel framework for 4D plant growth modeling that reconstructs the continuous geometric and topological evolution of plants from sparse temporal observations. Existing methods mainly rely on dense registration, yet reliable dense sequences are hard to obtain due to scanning constraints and self-occlusions, leaving these approaches struggling under large temporal gaps where rapid organ emergence violates local rigidity. To overcome this, we bridge these gaps by formulating plant morphogenesis as a continuous procedural process on a structure-aware Riemannian growth field; this jointly models topology evolution and geometric deformation, preserving botanical hierarchies and stable spatio-temporal correspondences across distant timepoints. Our key idea is to ground symbolic growth rules within a continuous geodesic flow, where organ development follows biologically modulated trajectories that preserve structural coherence under topological changes. We further contribute a 10-day dual-species dataset with dense geometric and semantic annotations. Experiments demonstrate that our method accurately tracks individual organ growth over time and significantly outperforms state-of-the-art baselines in both geometric accuracy and correspondence consistency.
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