# Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction
## 4 Discussion
### Point-Cloud Downsampling Experiment and Analysis Based on FFN Output Facets
#### 4.1.3 Quantitative Comparison Results and Analysis  
(1) Semantic Segmentation Performance  
Based on the aforementioned experiments, the metric results for the semantic segmentation task using the four downsampling strategies are presented in Table 3.  
From the results above, it can be observed that the downsampling method based on FFN patches achieves the best performance on the two key comprehensive metrics, F1-score and IoU. Specifically, the average F1-score reaches 93.49%, outperforming FPS (93.34%), random sampling (93.06%), and voxelization + FPS (93.14%); the average IoU reaches 88.14%, which is also higher than that of the other strategies. This is mainly attributed to the fact that FFN patch downsampling, while ensuring a uniform global point-cloud distribution, more reliably preserves the boundary features of each semantic region. For example, in the tomato leaf region, which in the original point cloud often appears highly curved and overlapping, random or voxelized sampling tends to miss edge-dense areas, making it difficult for the segmentation network to identify them accurately. In contrast, FFN patch downsampling already refines geometric and semantic information within patches, and the projected representative points better conform to the overall leaf shape, significantly improving Recall (97.58%) and IoU (95.38%). In the stem category, the FFN method also achieves an IoU of 79.14%, exceeding other methods by approximately 0.4-1.4 percentage points, indicating that in regions with complex curved surfaces and slender structures, FFN patch downsampling more effectively captures critical geometric features, enabling the semantic segmentation network to achieve higher sensitivity and accuracy for smaller or thinner semantic segments. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Tobacco**} & \multicolumn{3}{c}{**Tomato**} & \multicolumn{3}{c}{**Sorghum**} \\ \hline Metric & Method & Stem & Leaf & Stem & Leaf & Stem & Leaf & Mean \\  & Method of this paper & 89.97 & 95.05 & 96.15 & 97.70 & 83.75 & 97.76 & 93.40 \\ P & FPS & 89.49 & 95.32 & 95.92 & 96.76 & 87.71 & 97.31 & 93.75 \\  & Random & 87.36 & 94.33 & 95.80 & 97.10 & 86.61 & 97.08 & 93.05 \\  & Voxel + FPS & 88.95 & 95.09 & 96.06 & 96.76 & 86.31 & 96.74 & 93.32 \\  & Method of this paper & 86.80 & 95.89 & 96.41 & 97.58 & 87.70 & 97.30 & 93.61 \\ Re & FPS & 85.60 & 92.87 & 96.05 & 97.55 & 87.81 & 97.80 & 92.95 \\  & Random & 88.35 & 93.60 & 95.74 & 96.89 & 86.08 & 97.84 & 93.08 \\  & Voxel + FPS & 87.05 & 93.44 & 95.54 & 97.09 & 86.99 & 97.67 & 92.96 \\  & Method of this paper & 88.36 & 95.47 & 96.28 & 97.64 & 85.68 & 97.53 & 93.49 \\ F1 & FPS & 87.50 & 94.08 & 95.99 & 97.16 & 87.76 & 97.55 & 93.34 \\  & Random & 87.85 & 93.96 & 95.77 & 96.99 & 86.34 & 97.46 & 93.06 \\  & Voxel + FPS & 87.99 & 94.26 & 95.80 & 96.93 & 86.65 & 97.21 & 93.14 \\  & Method of this paper & 79.14 & 91.33 & 92.83 & 95.38 & 74.95 & 95.18 & 88.14 \\ IoU & FPS & 77.78 & 88.82 & 92.28 & 94.47 & 78.19 & 95.22 & 87.79 \\  & Random & 78.33 & 88.62 & 91.88 & 94.17 & 75.96 & 95.05 & 87.34 \\  & Voxel + FPS & 78.56 & 89.13 & 91.94 & 94.04 & 76.44 & 94.56 & 87.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of semantic segmentation metrics for four downsampling methods (%). From the results above, it is clear that the downsampling method based on FFN patches continues to lead in mCov, mWCov, and mRec, with average mCov of 83.19%, mWCov of 86.22%, and mRec of 88.19%, all significantly higher than FPS, random sampling, and voxelization + FPS. The improvements in mCov and mWCov are mainly due to the uniform downsampling of the FFN patch, which preserved key points within each instance, resulting in more complete instance coverage. At the same time, the increase in mRec reflects that more ground-truth instances are correctly matched in the predictions, validating that the representative points extracted by the FFN have a higher discriminative power between different instances.