# Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction
## 5 Conclusions  
This paper proposes a point cloud downsampling and segmentation strategy based on FFN-generated patches, effectively addressing the trade-offs among controllable point count, global uniformity, and local geometric fidelity. In downsampling, this method uses a fixed number of patches generated by the FFN (e.g., 1024) to achieve precise control over the point count, selecting the point closest to the geometric center within each patch as its representative. Experiments demonstrate that this strategy attains an average F1-score of 93.49% and an average IoU of 88.14% in the semantic segmentation task, significantly outperforming FPS (93.34%/87.79%) and random sampling (93.06%/87.34%). In complex-structure regions (tomato leaf IoU 95.38%) and slender organs (tobacco stem IoU 79.14%), because patches adhere closely to semantic boundaries, representative points fully preserve key geometric features, avoiding sampling bias in locally sparse regions.  
In the segmentation task, the FFN-patch-based clustering algorithm--through boundary-patch constraints and fragment-integration mechanisms--significantly enhances robustness under occlusion. Point-level visualizations (Figures 8-13) show that the proportion of correct leaf-edge points (CPs) increases by over 20% compared to method [23], with a reduction in the number of omitted points (MPs). Organ-level evaluation indicates that for tobacco samples, this method achieves 100% instance recall and precision, and for tomato samples, 100% as well (Table 5, whereas DBSCAN exhibits severe missed detections in sorghum samples (only 40% recall). This performance stems from the strict restriction on merging across semantic boundaries during patch growth and the fragment-merging mechanism that completes the missing leaf regions (e.g., sorghum leaf mWCov 86.18%). The strong generalization of the method is validated on three morphologically distinct crops: tobacco, tomato, and sorghum. Even when downsampled to 256 points, the downsampled point clouds clearly preserve details, such as tobacco leaf veins; in sorghum stem segmentation, the instance recall reaches 95.87%, covering almost all ground-truth instances. This offers a high-precision 3D solution for plant phenotyping analysis, overcoming the limitations of 2D images under occlusion and depth-missing scenarios.  
Future work will explore the end-to-end joint optimization of FFN patch generation and downsampling and design multi-scale feature supplementation mechanisms for extremely sparse scenarios (e.g., loss of stem-tip details at 256 points), further enhancing the method's applicability in fine-grained agricultural applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Inference Time** & **Peak GPU Memory** & \multirow{2}{*}{**FLOPs (M)**} \\  & **(ms)** & **(GB)** & \\ \hline FFN (ours) & 128/580/1120 & 4.2 & 380 \\ SLIC [6] & 85/410/920 & 1.8 (CPU) & 120 \\ DBSCAN & 186/1200/4500 & 2.5 (CPU) & 210 \\ PointNet++ [19] & 215/890/1850 & 5.1 & 487 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Computational cost comparison across methods (10k/50k/100k points).  
**Author Contributions:** [PERSON] designed the theoretical framework; [PERSON] and [PERSON] conducted the experiments; [PERSON] and [PERSON] performed a part of the data processing; [PERSON] organized the data and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author (Data needs to be inquired).  
**Conflicts of Interest:** Author [PERSON] is employed by the company China Telecom. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.