# Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction
## 4 Discussion
### Point-Cloud Segmentation via Clustering on FFN-Generated Patches  
Accurate segmentation of crop organs is a core task in plant phenotyping research, as it helps assess the growth status and pest or disease risk. Although 2D image segmentation techniques are mature, they remain prone to errors in complex backgrounds, under occlusion, and when depth information is lacking. Three-dimensional point clouds provide complete spatial geometric information that can partially mitigate occlusion effects. However, existing point-level feature segmentation methods are easily constrained by noise and uneven point cloud densities, resulting in unstable local segmentation. Hence, this experiment employs the fixed-number, regularly structured patches generated by FFN and applies patch-level clustering and diffusion strategies to achieve precise segmentation of crop leaves and stems, thereby improving performance under clustered leaf distributions and occlusion scenarios. Figure 6 illustrates the overall clustering-based segmentation pipeline.  
Figure 5: Visualization of three-scale downsampling based on FFN-generated patches for tobacco, tomato, and sorghum.  
Figure 6: Overall framework for FFN-based point cloud segmentation.  
As shown above, the FFN-based point cloud segmentation pipeline consists of the following steps:  
1. Boundary-patch extraction: First, iterate over each point, search for its nearest neighbors, and compute the normal vector of the point set via principal component analysis (PCA). Project the neighboring vectors onto the plane defined by the normal vector and compute the angles between adjacent projections. If the projection angle difference exceeds a certain threshold, the point is classified as a boundary point. After extracting all boundary points, the corresponding FFN patches are marked as boundary patches to serve as initial seeds for subsequent patch-based region growth.
2. Initial patch growth: Once the boundary patches are obtained, each non-boundary, unprocessed patch is treated as a seed, and neighboring patches within a specified search radius whose patch centers lie within a distance threshold from the seed's plane are merged using a breadth-first search strategy. Boundary patches act only as passive \"growth\" targets and are not permitted to expand actively, effectively preventing growth across sharp semantic boundaries. This process is repeated until all non-boundary patches are marked as belonging to the same connected region, forming preliminary leaf or stem clusters.
3. Secondary growth for unmerged boundary patches: After the initial growth, some boundary patches may remain unmerged, typically located in complex or steep local structures. For these remaining patches, a second breadth-first search is performed using the same search radius and distance threshold, removing the \"passive growth only\" restriction, and connecting them with the nearest already labeled patches. This ensures that all boundary patches are ultimately assigned to adjacent regions, thereby avoiding omissions.
4. Integration of leaf fragments and stem system: Based on the secondary growth results, separate clusters by a point-count threshold: any patch cluster with fewer points than the threshold is identified as a leaf fragment; otherwise, it is classified as part of the stem system (which may include entirely new leaves). Next, for each leaf fragment, the nearest adjacent leaf is searched among the set of large leaves obtained in the initial growth, and the qualifying fragments are merged into the corresponding leaf to complete the leaf edges. If a fragment does not connect to any large leaf, it is retained as an independent small leaf. Finally, the integrated leaf and stem clusters are merged to obtain the complete crop-organ segmentation result.