改进智能树算法，精准提取葡萄藤三维结构
Accurate 3D Grapevine Structure Extraction from High-Resolution Point Clouds
- 基于图模型解决葡萄藤骨架提取歧义问题
- 在真实数据上提升F1分数15.8%
- 适合智慧葡萄园管理与自动化修剪
精准的葡萄藤三维建模对精准葡萄栽培至关重要，尤其有助于科学修剪和自动化管理。然而，葡萄藤复杂的结构给传统骨架化算法带来挑战。本文针对葡萄藤特性，改进Smart-Tree算法，提出一种基于图的方法以消除骨架歧义。该方法能准确分离出单根藤蔓骨架，为精确分析与管理提供支持。我们在标注的真实葡萄藤点云数据上验证了该方法，相比原始Smart-Tree算法，F1得分提升15.8%。本研究推动了葡萄藤三维建模技术的发展，有望通过更精准、自动化的葡萄栽培实践提升生产可持续性与效益。
原文摘要 · Abstract (English)
Accurate 3D modelling of grapevines is crucial for precision viticulture, particularly for informed pruning decisions and automated management techniques. However, the intricate structure of grapevines poses significant challenges for traditional skeletonization algorithms. This paper presents an adaptation of the Smart-Tree algorithm for 3D grapevine modelling, addressing the unique characteristics of grapevine structures. We introduce a graph-based method for disambiguating skeletonization. Our method delineates individual cane skeletons, which are crucial for precise analysis and management. We validate our approach using annotated real-world grapevine point clouds, demonstrating improvement of 15.8% in the F1 score compared to the original Smart-Tree algorithm. This research contributes to advancing 3D grapevine modelling techniques, potentially enhancing both the sustainability and profitability of grape production through more precise and automated viticulture practices
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