# Structure-aware Riemannian Growth Fields for 4D Plant Modeling

arXiv: https://arxiv.org/abs/2608.13007 · HTML: https://mlsanity.com/paper/2608.13007/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 13 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [gsplat: An Open-Source Library for Gaussian Splatting](https://mlsanity.com/paper/2409.06765/) (2024)
- [CherryPicker: Semantic Skeletonization and Topological Reconstruction of Cherry Trees](https://mlsanity.com/paper/2304.04708/) (2023)
- [Wheat3DGS: In-field 3D Reconstruction, Instance Segmentation and Phenotyping of Wheat Heads with Gaussian Splatting](https://mlsanity.com/paper/2504.06978/) (2025)
- [NeuraLeaf: Neural Parametric Leaf Models with Shape and Deformation Disentanglement](https://mlsanity.com/paper/2507.12714/) (2025)
- [A Riemannian Approach for Spatiotemporal Analysis and Generation of 4D Tree-shaped Structures](https://mlsanity.com/paper/2408.12443/) (2024)
- [Probabilistic Plant Modeling via Multi-View Image-to-Image Translation](https://mlsanity.com/paper/1804.09404/) (2018)
- [PlantDreamer: Achieving Realistic 3D Plant Models with Diffusion-Guided Gaussian Splatting](https://mlsanity.com/paper/2505.15528/) (2025)
- [Learning to Infer Parameterized Representations of Plants from 3D Scans](https://mlsanity.com/paper/2505.22337/) (2025)
- [GaussianPlant: Structure-aligned Gaussian Splatting for 3D Reconstruction of Plants](https://mlsanity.com/paper/2512.14087/) (2025)

## Abstract (arXiv)

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.