Scale Matters: Adaptive Granularity Selection for Cross-Species 3D Plant Organ Segmentation
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 segme...
| Authors | Carla Salazar, Lazaros Nalpantidis |
|---|---|
| Primary category | cs.CV |
| Published | 18 August 2026 |
| Last updated at source | 19 August 2026 |
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