Non-Destructive 3D Root Structure Modeling

Abstract

Deep neural networks (DNNs) have gained significant attention in 3D object reconstruction. However, detecting and reconstructing hidden or buried objects underground remains a challenging task. Ground Penetrating Radar (GPR) has emerged as a cost-effective and non-destructive technology for subsurface object detection, including soil structures and pipelines. In this study, we present a deep convolutional neural network-based method for detecting target signals and performing curve parameter regression using multiple B-scans from GPR data. By leveraging the detection and regression outcomes, we further generate fitted curves that represent underground structures. To reconstruct a comprehensive and detailed 3D root structure, we design a shape reconstruction network that takes sparse sliced 3D points as input. The proposed approach is extensively trained and validated using synthetic 3D root datasets and simulated GPR data generated with gprMax. Additionally, the trained model demonstrates strong generalization capabilities when applied to real-world GPR data, ensuring its practical applicability.