Procedural Generation of 3D Maize Plant Architecture from LIDAR Data

TL;DR

This paper presents a novel framework combining Particle Swarm Optimization and differentiable programming to generate accurate 3D models of maize plants from LiDAR data, improving phenotyping capabilities.

Contribution

It introduces a hierarchical optimization approach using NURBS surfaces for detailed 3D maize plant reconstruction from LiDAR data, with open-source code for accessibility.

Findings

PSO provides a reliable initial surface fit.

Differentiable NURBS enhances surface accuracy.

Method works across diverse maize genotypes.

Abstract

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.