Structure-aware Riemannian Growth Fields for 4D Plant Modeling

2026-08-13

AI Summary

This work addresses the challenge of modeling rapid organ emergence, drastic topological changes, and self-occlusion in 4D plant growth under sparse observations and large temporal gaps. The authors propose to formulate plant morphogenesis as a continuous process on a structure-aware Riemannian growth field, where symbolically guided geodesic flows jointly optimize geometric deformation and topological evolution to establish stable spatiotemporal correspondences across time points. By integrating structure-aware Riemannian geometry with symbolic growth rules—a first in the field—the method preserves structural consistency despite topological alterations. The study also introduces the first high-annotation-density dataset spanning ten days across two plant species. Experiments demonstrate that the proposed approach significantly outperforms existing methods in both geometric accuracy and organ correspondence consistency, enabling precise long-term tracking of individual organ growth trajectories even from sparse observations.

Abstract

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.

Problem

• 4D plant modeling
• sparse temporal observations
• topology evolution
• geometric deformation
• temporal gaps

Innovation

• Riemannian growth fields
• 4D plant modeling
• topology evolution
• structure-aware modeling
• geodesic flow