Demeter: A Parametric Model of Crop Plant Morphology from the Real World (ICCV 2025)

Project Page | Mesh Dataset

Demeter is a plant parametric models that is learned from 3D scans of real-world plants. It explicitly models the plant as a graph of stem and leaf.

1. Data

Processed data

The processed 3d parametric plant samples are already included in the code.

Raw data

The raw soybean mesh data can be found in this google drive link. It contains 607 unprocessed meshes, which can be used for 3D generation/representation learning. The main stem are aligned to y-axis and the bottom tip lies in (0,0,0). We will release the correspondent 2D images soon.

2. Requirements

Environment (Tested)

• Linux
• Python 3.11
• CUDA 12.1
• Pytorch 2.5.0

Dependencies

Install PyTorch and other dependencies.

conda create -n demeter python=3.11 -y
conda activate demeter
pip install torch==2.5.0 torchvision==0.20.0 torchaudio==2.5.0 --index-url https://download.pytorch.org/whl/cu121

# basic dependencies for decoding
pip install -r requirements.txt

Install in editable mode

pip install -e .

for reconstruction from 3d point cloud, it is recommended to create a new envrionment following instruction in Pointcept. But we recommend using manual annotation to create demeter parameters for now.

3. Usage

a) Visualize parametric plant

decode demeter parameter to 3d mesh of soybean

python decode.py --data_folder sample_params --sample_name 24_o --species soybean

python decode.py --data_folder sample_params --sample_name 08 --species ribes 

python decode.py --data_folder sample_params --sample_name 10008da --species maize

python decode.py --data_folder sample_params --sample_name 1 --species tobacco

python decode.py --data_folder sample_params --sample_name 02 --species rose

b) Reconstruction parametric plant from point cloud

• (Optional) Step 0: Fitting 2D leaf shape from images if you need other species script_process_leaf_contour/readme.md

• Step 1: Get point cloud Monocular RGB video -> Raw 3D point clouds
  third_party/2d-gaussian-splatting/readme.md

• Step 2, Option A: manual annotate point clouds -> Demeter parameters
  script_manual_annotation/readme.md

• Step 2, Option B: automatic multi-stage 3D point clouds -> Demeter parameters
  script_auto_reconstruction/readme.md Note that this method is not accurate, so it's more recommanded to use manual segementation

• Step 2, Option C: automatic feed-forward one-pass 3D point clouds -> Demeter parameters
  Working in progress.

• Others: raw 3D point clouds -> baseline L-system parameteres
  third_party/CropCraft/readme.md

c) Simulation

Please refer to Helios Tutorial for now.

4. Release Note

• editing tutorial (TBD)
• full soybean 2d image dataset (TBD)
• learning leaf shape PCA from 2D leaf scanns (2026-5-26)
• building demeter representation from your own annotated 3d point cloud (2026-4-24)
• full soybean 3d dataset (2025-12-17)
• sample data of other species (2025-11-1)
• sample data of soybean (2025-10-7)
• decoding (2025-10-7)
• reconstruction from 3d point cloud (2025-10-8)
• L-system baseline (2025-10-13)

5. Acknowledgement

This project is supported by NSF Awards #1847334 #2331878, #2340254, #2312102, #2414227, and #2404385. We greatly appreciate the NCSA for providing computing resources.

6. License

This code is released under the Academic Research License (Non-Commercial). For commercial inquiries, please contact shenlong@illinois.edu. For code issue and academic collaboration, please contact tcheng12@illinois.edu.