# Procedural Generation of 3D Maize Plant Architecture from LIDAR Data

arXiv: https://arxiv.org/abs/2501.13963 · HTML: https://mlsanity.com/paper/2501.13963/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 93 references that are in the map.
- First posted on arXiv 21 Jan 2025.

## Builds on (most cited first, 17 of 17)

- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generation](https://mlsanity.com/paper/1802.05384/) (2018)
- [SAL: Sign Agnostic Learning of Shapes from Raw Data](https://mlsanity.com/paper/1911.10414/) (2019)
- [Local Implicit Grid Representations for 3D Scenes](https://mlsanity.com/paper/2003.08981/) (2020)
- [PU-GAN: a Point Cloud Upsampling Adversarial Network](https://mlsanity.com/paper/1907.10844/) (2019)
- [Deep Geometric Prior for Surface Reconstruction](https://mlsanity.com/paper/1811.10943/) (2018)
- [PCPNET: Learning Local Shape Properties from Raw Point Clouds](https://mlsanity.com/paper/1710.04954/) (2017)
- [GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer](https://mlsanity.com/paper/2308.03768/) (2023)
- [Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks](https://mlsanity.com/paper/1812.00709/) (2018)
- [Surface Reconstruction from Point Clouds: A Survey and a Benchmark](https://mlsanity.com/paper/2205.02413/) (2022)
- [SSRNet: Scalable 3D Surface Reconstruction Network](https://mlsanity.com/paper/1911.07401/) (2019)
- [PointTriNet: Learned Triangulation of 3D Point Sets](https://mlsanity.com/paper/2005.02138/) (2020)
- [DeepDT: Learning Geometry From Delaunay Triangulation for Surface Reconstruction](https://mlsanity.com/paper/2101.10353/) (2021)
- [NURBS-Diff: A Differentiable Programming Module for NURBS](https://mlsanity.com/paper/2104.14547/) (2021)
- [Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives](https://mlsanity.com/paper/1804.05319/) (2018)
- [Automated Reconstruction of 3D Open Surfaces from Sparse Point Clouds](https://mlsanity.com/paper/2210.15059/) (2022)

## Built on by (most cited first, 4 of 4)

- [Demeter: A Parametric Model of Crop Plant Morphology from the Real World](https://mlsanity.com/paper/2510.16377/) (2025)
- [MaizeField3D: A Curated 3D Point Cloud and Procedural Model Dataset of Field-Grown Maize from a Diversity Panel](https://mlsanity.com/paper/2503.07813/) (2025)
- [FloraForge: LLM-Assisted Procedural Generation of Editable and Analysis-Ready 3D Plant Geometric Models For Agricultural Applications](https://mlsanity.com/paper/2512.11925/) (2025)
- [A Vision-Language Model (VLM)-based Pipeline for End-to-End Procedural Modeling of Field-Grown Maize from Point Clouds](https://mlsanity.com/paper/2610.03468/) (2026)

## Abstract (arXiv)

This study introduces a robust framework for generating procedural 3D models of maize (Zea mays) plants from LiDAR point cloud data, offering a scalable alternative to traditional field-based phenotyping. Our framework leverages Non-Uniform Rational B-Spline (NURBS) surfaces to model the leaves of maize plants, combining Particle Swarm Optimization (PSO) for an initial approximation of the surface and a differentiable programming framework for precise refinement of the surface to fit the point cloud data. In the first optimization phase, PSO generates an approximate NURBS surface by optimizing its control points, aligning the surface with the LiDAR data, and providing a reliable starting point for refinement. The second phase uses NURBS-Diff, a differentiable programming framework, to enhance the accuracy of the initial fit by refining the surface geometry and capturing intricate leaf details. Our results demonstrate that, while PSO establishes a robust initial fit, the integration of differentiable NURBS significantly improves the overall quality and fidelity of the reconstructed surface. This hierarchical optimization strategy enables accurate 3D reconstruction of maize leaves across diverse genotypes, facilitating the subsequent extraction of complex traits like phyllotaxy. We demonstrate our approach on diverse genotypes of field-grown maize plants. All our codes are open-source to democratize these phenotyping approaches.