Demeter通过学习真实作物形态，实现可参数化的植物3D建模。
Demeter: A Parametric Model of Crop Plant Morphology from the Real World
- 基于真实农田数据构建植物形态的紧凑参数化表示
- 支持不同物种间拓扑变化及三类形变的建模
- 适用于作物重建、生成与生物物理过程模拟
3D参数化形状建模在视觉与图形学中日益流行，广泛应用于3D重建、生成、理解与仿真。尽管人类和动物已有强大模型，但植物领域的表达性方法仍不足。本文提出Demeter，一种数据驱动的参数化模型，将植物形态的关键因素——拓扑、形状、关节结构与形变——编码为紧凑的可学习表示。与以往模型不同，Demeter能处理跨物种的拓扑差异，并建模三种形状变化来源：关节运动、子部件形状变异与非刚性形变。为推进作物建模，研究团队在大豆农场采集大规模带真实标注的数据集作为测试基准。实验表明，Demeter在形状合成、结构重建及生物物理过程仿真方面均表现优异。代码与数据已公开于https://tianhang-cheng.github.io/Demeter/。
原文摘要 · Abstract (English)
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/.
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