Demeter: A Parametric Model of Crop Plant Morphology from the Real World
This work addresses the problem of modeling plant morphology for applications in agriculture and simulation, representing a novel method for a known bottleneck in the field.
The authors tackled the lack of expressive parametric models for plants by developing Demeter, a data-driven model that encodes plant morphology factors like topology and deformation, and demonstrated its effectiveness in synthesizing shapes and reconstructing structures using a large-scale soybean dataset.
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/.