GrowFields: Compositional 4D Neural Fields for Topology-Changing Plant Growth

Quantifying plant growth dynamics from sparse longitudinal3D observations is fundamental for agriculture and plant sciences. Yet,plants pose unique challenges: they undergo intricate non-rigid deforma-tions, exhibit changing topology as new organs emerge, and often lackexplicit temporal correspondences between consecutive data acquisitionsdue to newly formed tissue. Methods designed for general scenes struggleto model topology changes and asynchronous organ growth characteris-tic of plants. To address these challenges, we introduce GrowFields, acompositional dynamic neural field representation for organ-aware 4Dplant growth modelling from point cloud time series. Our approach de-composes a plant into its constituent organs and aligns each organ intoits own canonical coordinate frame, isolating intrinsic growth patternsfrom global plant motion. We then learn a shared continuous neural de-formation field that models temporal dynamics across all organs, condi-tioned on learnable per-organ latent codes capturing organ identity andgrowth characteristics. The resulting modular yet unified representationnaturally accommodates the asynchronous development of plant organswhile remaining grounded in the practical setting of organ-level planttracking. We evaluate GrowFields on growth sequences from four plantspecies, assessing geometric fitting and organ tracking accuracy usingmanually annotated leaf-tip trajectories. Results demonstrate consistentimprovements in spatial precision, temporal coherence, and morphologi-cal fidelity over a range of existing representations.