地下植物探索：基于点图神经网络的探地雷达非破坏性根系三维评估
Underground Plant Exploration: Non-Destructive 3D Root Assessment with GPR Based on Point Graph Neural Network
将 GPR 多 B-scan 中的双曲回波先验与曲线参数回归结合，得到稀疏三维根切片，再用点图网络加上采样模块补全完整根系；对视觉研究者而言，这是一套从稀疏二维扫描到稠密三维结构的重建范式，可迁移到其他稀疏观测下的三维形状补全任务。
原文依据 we design a root shape modeling network that processes sparse 3D slices using a specialized point graph network and an upsampling module
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文提出一种用于植物根系结构非破坏性三维建模的创新方法，根系结构对养分与水分的吸收至关重要。探地雷达（GPR）已被用于探测管道等形状明确的地下目标，但准确重建复杂的根系结构仍是一项重大挑战。为此，我们提出一种新颖框架，利用 GPR 信号形状先验在多个 B-scan 上完成目标信号检测与曲线参数回归。通过整合这些检测与回归结果，我们获得精确表示根系结构的双曲曲线。为进一步评估完整且精细的三维根系系统，我们设计了一个根系形状建模网络，该网络使用专门的点图网络与一个上采样模块处理稀疏三维切片。该方法可扩展至土木工程、地质学与环境监测等众多应用。
英文原文摘要 961 字符
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.