GrowFields：用于拓扑变化植物生长的组合式4D神经场
GrowFields: Compositional 4D Neural Fields for Topology-Changing Plant Growth
AI总结：
针对从稀疏纵向3D观测量化植物生长动力学的难题，提出GrowFields，将植物分解为器官并对齐到规范坐标系，学习共享神经变形场，在植物器官跟踪中表现优于现有表示。
摘要：
从稀疏纵向3D观测中量化植物生长动力学对农业和植物科学至关重要。但植物有独特挑战，现有方法难以应对。为此引入GrowFields，一种用于从点云时间序列进行器官感知4D植物生长建模的组合动态神经场表示，评估显示其优于现有表示。
英文摘要：
Quantifying plant growth dynamics from sparse longitudinal 3D observations is fundamental for agriculture and plant sciences. Yet, plants pose unique challenges: they undergo intricate non-rigid deformations, exhibit changing topology as new organs emerge, and often lack explicit temporal correspondences between consecutive data acquisitions due to newly formed tissue. Methods designed for general scenes struggle to model topology changes and asynchronous organ growth characteristic of plants. To address these challenges, we introduce GrowFields, a compositional dynamic neural field representation for organ-aware 4D plant growth modelling from point cloud time series. Our approach decomposes a plant into its constituent organs and aligns each organ into its own canonical coordinate frame, isolating intrinsic growth patterns from global plant motion. We then learn a shared continuous neural deformation field that models temporal dynamics across all organs, conditioned on learnable per-organ latent codes capturing organ identity and growth characteristics. The resulting modular yet unified representation naturally accommodates the asynchronous development of plant organs while remaining grounded in the practical setting of organ-level plant tracking. We evaluate GrowFields on growth sequences from four plant species, assessing geometric fitting and organ tracking accuracy using manually annotated leaf-tip trajectories. Results demonstrate consistent improvements in spatial precision, temporal coherence, and morphological fidelity over a range of existing representations.
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