# Learning to Infer Parameterized Representations of Plants from 3D Scans

arXiv: https://arxiv.org/abs/2505.22337 · HTML: https://mlsanity.com/paper/2505.22337/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 59 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 12 of 12)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Im2Struct: Recovering 3D Shape Structure from a Single RGB Image](https://mlsanity.com/paper/1804.05469/) (2018)
- [PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation](https://mlsanity.com/paper/1903.00709/) (2019)
- [SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation with Fine-Grained Geometry](https://mlsanity.com/paper/2302.10237/) (2023)
- [CherryPicker: Semantic Skeletonization and Topological Reconstruction of Cherry Trees](https://mlsanity.com/paper/2304.04708/) (2023)
- [VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis](https://mlsanity.com/paper/2307.03592/) (2023)
- [Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods](https://mlsanity.com/paper/2012.11489/) (2020)
- [Smart-Tree: Neural Medial Axis Approximation of Point Clouds for 3D Tree Skeletonization](https://mlsanity.com/paper/2303.11560/) (2023)
- [PLANesT-3D: A new annotated dataset for segmentation of 3D plant point clouds](https://mlsanity.com/paper/2407.21150/) (2024)
- [Machine Vision System for 3D Plant Phenotyping](https://mlsanity.com/paper/1705.00540/) (2017)

## Built on by (most cited first, 3 of 3)

- [GrowFields: Compositional 4D Neural Fields for Topology-Changing Plant Growth](https://mlsanity.com/paper/2607.03330/) (2026)
- [Structure-aware Riemannian Growth Fields for 4D Plant Modeling](https://mlsanity.com/paper/2608.13007/) (2026)
- [ProxyBuild: Text-Guided Structured 3D Building Generation with Mesh-Anchored Procedural Proxies](https://mlsanity.com/paper/2609.23386/) (2026)

## Abstract (arXiv)

Plants frequently contain numerous organs, organized in 3D branching systems defining the plant's architecture. Reconstructing the architecture of plants from unstructured observations is challenging because of self-occlusion and spatial proximity between organs, which are often thin structures. To achieve the challenging task, we propose an approach that allows to infer a parameterized representation of the plant's architecture from a given 3D scan of a plant. In addition to the plant's branching structure, this representation contains parametric information for each plant organ, and can therefore be used directly in a variety of tasks. In this data-driven approach, we train a recursive neural network with virtual plants generated using a procedural model. After training, the network allows to infer a parametric tree-like representation based on an input 3D point cloud. Our method is applicable to any plant that can be represented as binary axial tree. We quantitatively evaluate our approach on Chenopodium Album plants on reconstruction, segmentation and skeletonization, which are important problems in plant phenotyping. In addition to carrying out several tasks at once, our method achieves results on-par with strong baselines for each task. We apply our method, trained exclusively on synthetic data, to 3D scans and show that it generalizes well.