GrowFields: Compositional 4D Neural Fields for Topology-Changing Plant Growth
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 decompo...
| Authors | Joaquin Gajardo, Michele Volpi, Marko Mihajlovic et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 July 2026 |
| Last updated at source | 9 July 2026 |
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