Abstract
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 acquisitions due to newly formed tissue. GrowFields is a compositional dynamic neural field for organ-aware 4D plant growth modelling from point cloud time series that decomposes a plant into its constituent organs and learns a shared continuous neural deformation field conditioned on per-organ latent codes.
