Grow with the Flow: 4D Reconstruction of Growing Plants with Gaussian Flow Fields

Abstract

Modeling the time-varying 3D appearance of plants during growth poses unique challenges: unlike most dynamic scenes, plants continuously generate new geometry as they expand, branch, and differentiate. Existing dynamic scene representations are ill-suited to this setting: deformation fields provide insufficient constraints to yield physically plausible scene dynamics, and 4D Gaussian splatting represents the same physical structures with different Gaussian primitives at different times, breaking temporal consistency. We introduce GrowFlow, a dynamic representation that couples 3D Gaussian primitives with a neural ordinary differential equation to model plant growth as a continuous flow field over geometric parameters (position, scale, and orientation). Our representation enables consistent appearance rendering and models nonlinear, continuous-time growth dynamics with full temporal correspondences for every primitive. To initialize a sufficient set of Gaussian primitives, we first reconstruct the mature plant and then learn a reverse-growth process, effectively simulating the plant's developmental history in reverse. GrowFlow achieves superior image quality and geometric coherence compared to prior methods on a new, multi-view timelapse dataset of plant growth, and provides the first temporally coherent representation for appearance modeling of growing 3D structures.

Plant phenotyping relevance

植物の成長を対象に、4D再構成と連続的な成長表現を開発し、幾何学的整合性と画像品質を既存手法・新規データセットで比較評価しているため、植物フェノタイピング手法が中心です。

Code and data availability

The paper introduces a new multi-view timelapse plant growth dataset (three real species plus seven synthetic Blender scenes) and the GrowFlow method, both directly relevant to this paper's phenotyping measurements and analysis. However, the authors only promise future release ('we will publicly release all code and数据'

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