用树约束图生成法，从图像中统一估计植物骨架结构。
PlantPose: Universal Plant Skeleton Estimation via Tree-constrained Graph Generation
- 基于学习的图生成结合传统算法，训练时强制树状拓扑约束。
- 在真实与合成图像上均实现高精度骨架估计，跨域泛化能力强。
- 适合智能农业、植物科学领域研究者使用，支持多种输入风格。
从图像中准确估计植物骨架结构（如分枝结构）对智慧农业和植物科学至关重要。与拓扑固定的人体骨架不同，植物骨架估计面临独特挑战：需从图像中推断任意树状图结构。为此，我们提出PlantPose，一种通过树约束图生成实现通用植物骨架估计的方法。该方法将基于学习的图生成与传统图算法结合，在训练过程中施加树形约束。为提升模型泛化能力，我们构建了一个大规模、多样化的数据集，包含真实世界和合成植物图像，以及简化的表示形式（如草图和抽象画）。该数据集使模型能适应多种输入风格和植物类别，同时保持拓扑一致性。我们的方法在多个领域（包括此前未见的域外场景）均表现出鲁棒且准确的骨架估计性能。进一步分析揭示了该方法在处理复杂、异构数据分布时的优势与局限。所有代码与数据集已开源，地址见https://github.com/huntorochi/PlantPose/。
原文摘要 · Abstract (English)
Accurate estimation of plant skeletal structures (e.g., branching structures) 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. To address this problem, we introduce PlantPose, a universal plant skeleton estimator via tree-constrained graph generation. PlantPose combines learning-based graph generation with traditional graph algorithms to enforce tree constraints during the training loop. To enhance the model's generalization capability, we curate a large and diverse dataset comprising real-world and synthetic plant images, along with simplified representations (e.g., sketches and abstract drawings). This dataset enables the generalized model to adapt to diverse input styles and categories of plant images while preserving topological consistency. Our approach demonstrates robust and accurate plant skeleton estimation across multiple domains, including previously unseen out-of-domain scenarios. Further analyses highlight the method's strengths and limitations in handling complex, heterogeneous data distributions. All implementations and datasets are available at https://github.com/huntorochi/PlantPose/.
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