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This paper introduces a framework for modeling 4D plant growth that reconstructs the geometric and topological evolution of plants from sparse temporal observations, addressing the limitations of existing methods that rely on dense registration. By formulating plant morphogenesis as a continuous procedural process on a structure-aware Riemannian growth field, the authors preserve botanical hierarchies and establish stable spatio-temporal correspondences, even amidst large temporal gaps. The proposed method significantly enhances tracking accuracy of individual organ growth over time, outperforming state-of-the-art techniques in geometric accuracy and correspondence consistency.
Bridging the gaps in plant growth modeling, this framework enables accurate tracking of organ development over time, even with sparse data.
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.
