用树约束生成植物骨架，提升图像识别准确率。
TreeFormer: Single-view Plant Skeleton Estimation via Tree-constrained Graph Generation
- 结合学习与传统算法，在训练中强制输出为树状图
- 在合成、根系和葡萄藤数据上均实现高精度估计
- 适合植物结构分析与智能农业研究者使用
从图像中准确估计植物骨架（如分枝结构）对智慧农业和植物科学至关重要。与拓扑固定的行人骨架不同，植物骨架估计需从图像中推断任意树状图。尽管近期图生成方法能成功提取细长结构，但严格约束输出为树结构仍具挑战。为此，我们提出TreeFormer，一种基于树约束图生成的植物骨架估计算法。该方法在训练过程中将无约束图投影到最小生成树（MST），并通过抑制无关特征值将先验知识融入梯度下降优化。实验表明，该方法在多个领域均能准确估计目标植物骨架：合成树形图案、真实植物根系及葡萄藤枝条。代码已开源：https://github.com/huntorochi/TreeFormer/
原文摘要 · Abstract (English)
Accurate estimation of plant skeletal structure (e.g., branching structure) from images is essential for smart agriculture and plant science. Unlike human skeletons with fixed topology, plant skeleton estimation presents a unique challenge, i.e., estimating arbitrary tree graphs from images. While recent graph generation methods successfully infer thin structures from images, it is challenging to constrain the output graph strictly to a tree structure. To this problem, we present TreeFormer, a plant skeleton estimator via tree-constrained graph generation. Our approach combines learning-based graph generation with traditional graph algorithms to impose the constraints during the training loop. Specifically, our method projects an unconstrained graph onto a minimum spanning tree (MST) during the training loop and incorporates this prior knowledge into the gradient descent optimization by suppressing unwanted feature values. Experiments show that our method accurately estimates target plant skeletal structures for multiple domains: Synthetic tree patterns, real botanical roots, and grapevine branches. Our implementations are available at https://github.com/huntorochi/TreeFormer/.
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