Problem
Research questions and friction points this paper is trying to address.
3D plant organ segmentation
spatial granularity
cross-species generalization
plant phenotyping
Scale Matters: Adaptive Granularity Selection for Cross-Species 3D Plant Organ Segmentation
🤖 AI Summary
为解决植物器官3D分割中固定空间粒度限制泛化的问题，提出AGS-PlantSeg方法，通过自适应选择粒度和利用预训练模型优化特征提取，显著提高跨物种分割性能。
📝 Abstract
Recent 3D foundation models provide powerful feature representations for point cloud learning by controlling spatial granularity. However, relying on a fixed spatial granularity severely limits generalization in applications like plant phenotyping, where organ morphology and size vary substantially across species and growth stages. To address this, we propose AGS-PlantSeg, a few-shot 3D plant organ segmentation method that leverages the frozen Utonia (arXiv:2603.03283) foundation model combined with Adaptive Granularity Selection. By dynamically selecting the best granularity levels for each specific plant model, our method extracts optimized geometric features for a lightweight MLP segmentation head. Extensive experiments across PLANesT-3D (arXiv:2407.21150), Pheno4D , and Crops3D demonstrate that AGS-PlantSeg significantly improves cross-species generalization, achieving 88.9% average mIoU performance and outperforming fixed-granularity baselines by 2.5 mIoU points. Despite requiring minimal annotated data, our approach is highly competitive with fully supervised, plant-specific architectures.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adaptive Granularity Selection
few-shot 3D plant organ segmentation
Utonia foundation model
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