Demeter: A Parametric Model of Crop Plant Morphology from the Real World

Abstract

Learning 3D parametric shape models of objects has gained popularity in vision and graphics and has showed broad utility in 3D reconstruction, generation, understanding, and simulation. While powerful models exist for humans and animals, equally expressive approaches for modeling plants are lacking. In this work, we present Demeter, a data-driven parametric model that encodes key factors of a plant morphology, including topology, shape, articulation, and deformation into a compact learned representation. Unlike previous parametric models, Demeter handles varying shape topology across various species and models three sources of shape variation: articulation, subcomponent shape variation, and non-rigid deformation. To advance crop plant modeling, we collected a large-scale, ground-truthed dataset from a soybean farm as a testbed. Experiments show that Demeter effectively synthesizes shapes, reconstructs structures, and simulates biophysical processes. Code and data is available at https://tianhang-cheng.github.io/Demeter/.

Plant phenotyping relevance

植物形態のトポロジー・形状・関節・変形を表現する3Dパラメトリックモデルを開発し、実世界の作物データセットで再構成・シミュレーションを評価しているため、形態フェノタイピング手法が中心である。

Code and data availability

The paper's own soybean dataset and code are referenced only via a project page with no URL supplied, so no actionable paper-specific asset is available. Other URLs in the text (figshare corn point clouds, detectron2, semantic scholar links) belong to cited prior work or generic libraries, not this paper's phenotyping/

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