Underground Plant Exploration: Non-Destructive 3D Root Assessment with GPR Based on Point Graph Neural Network

Yuwei Zhou, Guoyu Lu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026, pp. 15616-15626

This paper presents an innovative approach for non-destructive 3D modeling of plant root structures, which are essential for nutrient and water uptake. While Ground Penetrating Radar (GPR) has been used for detecting subsurface objects with well-defined shapes, such as pipes, accurately reconstructing complex root structures remains a significant challenge. To address this, we propose a novel framework that leverages GPR signal shape priors for target signal detection and curve parameter regression across multiple B-scans. By integrating these detection and regression results, we obtain precise hyperbolic curves representing root structures. To further assess complete and detailed 3D root systems, we design a root shape modeling network that processes sparse 3D slices using a specialized point graph network and an upsampling module. The method can be extended to many applications, including civil engineering, geology, and environmental monitoring.