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

• URL: http://arxiv.org/abs/2602.08958v2
• Date: Tue, 10 Feb 2026 15:00:19 GMT
• Title: Grow with the Flow: 4D Reconstruction of Growing Plants with Gaussian Flow Fields
• Authors: Weihan Luo, Lily Goli, Sherwin Bahmani, Felix Taubner, Andrea Tagliasacchi, David B. Lindell,
• Abstract summary: We introduce a 3D Gaussian flow field representation that models plant growth as a time-varying derivative over Gaussian parameters.<n>We reconstruct the mature plant and learn a process of reverse growth, effectively simulating the plant's developmental history in reverse.<n>Our approach achieves superior image quality and geometric accuracy compared to prior methods on multi-view timelapse datasets of plant growth.
• Score: 37.924718950550336
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modeling the time-varying 3D appearance of plants during their growth poses unique challenges: unlike many dynamic scenes, plants generate new geometry over time as they expand, branch, and differentiate. Recent motion modeling techniques are ill-suited to this problem setting. For example, deformation fields cannot introduce new geometry, and 4D Gaussian splatting constrains motion to a linear trajectory in space and time and cannot track the same set of Gaussians over time. Here, we introduce a 3D Gaussian flow field representation that models plant growth as a time-varying derivative over Gaussian parameters -- position, scale, orientation, color, and opacity -- enabling nonlinear and continuous-time growth dynamics. To initialize a sufficient set of Gaussian primitives, we reconstruct the mature plant and learn a process of reverse growth, effectively simulating the plant's developmental history in reverse. Our approach achieves superior image quality and geometric accuracy compared to prior methods on multi-view timelapse datasets of plant growth, providing a new approach for appearance modeling of growing 3D structures.

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