Procedural Generation of 3D Maize Plant Architecture from LIDAR Data

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.

Plant phenotyping relevance

LiDAR点群からトウモロコシ葉の3D形状を再構成し、表現型形質抽出に用いる計算手法を開発・実証しており、植物フェノタイピング手法が中心である。

Code and data availability

The paper states its code is open-source but provides no authors' repository URL or deposit identifier in the supplied blocks. The only allowed URL corresponds to PySwarms, a generic third-party PSO library cited in the references, not a paper-specific asset. The LiDAR point cloud dataset is described but no public URL

No evidence-backed public reproduction asset is currently recorded.