发表机构
Nara Women’s University; Kyoto University（奈良女子大学; 京都大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出结构感知黎曼生长场框架，用于从稀疏时间观测重建4D植物生长，构建了10天双物种标注数据集，在几何精度与对应一致性上优于现有方法。
AI 中文摘要
本文提出一种用于4D植物生长建模的新型框架，该框架可从稀疏时间观测中重建植物连续的几何与拓扑演化过程。现有方法主要依赖密集配准，但受扫描限制和自遮挡影响，难以获得可靠的密集序列，导致这些方法在时间间隔较大时表现不佳，此时器官的快速出现违背了局部刚性假设。为解决这一问题，我们将植物形态发生建模为结构感知黎曼生长场上的连续过程，以此弥合时间间隔，该模型可联合建模拓扑演化与几何变形，保留植物层级结构，并在相距较远的时间点间维持稳定的时空对应关系。我们的核心思路是将符号化生长规则建立在连续测地流中，使器官发育遵循经生物调制的轨迹，在拓扑变化下保持结构一致性。此外，我们还构建了一个包含10天观测数据的双物种数据集，该数据集带有密集的几何与语义标注。实验表明，我们的方法可准确追踪单个器官随时间的生长情况，在几何精度与对应一致性方面均显著优于现有基准方法。
英文摘要
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.
CommentsAccepted to WACV 2027 (Round 1)
