# 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
Although mPrec is slightly inferior to random sampling in certain categories, FFN patch downsampling still demonstrates a more robust overall performance in most categories, especially in terms of achieving a higher mPrec in multiple categories of tomato and sorghum. Specifically, for the sorghum \"leaf\" category, the FFN strategy obtains an mWCov of 86.18%, an increase of nearly 1.5 percentage points over voxelization + FPS; for the sorghum stem category, mRec reaches 95.87%, indicating almost complete coverage of all ground-truth instances. This demonstrates that FFN patch downsampling significantly enhances the ability to represent instance geometry, particularly in cross-instance boundary segmentation tasks, thereby helping to reduce instance label confusion and missed detections. (3) Visualization Analysis  
Figure 5 shows the visualization results of the FFN patch downsampling on typical samples of tobacco, tomato, and sorghum. In Figure 5, the first column shows the original point-cloud instance labels (10,000 points), while the second, third, and fourth columns display the results of downsampling to 1024, 512, and 256 points via the FFN network. As shown, regardless of the extent to which the sampling density is reduced, the FFN-based patch downsampling preserves both the geometric silhouette of the point cloud and its semantic distribution. Even when reduced to 256 points, the venation pattern in the tobacco leaf region remains clearly visible. At 512 points, the boundary between the tomato fruit and stem could still be distinctly distinguished. These visual results further confirm that FFN-based patch downsampling not only excels in quantitative metrics but also retains the key information \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **Tobacco** & & **Tomato** & & **Sorghum** & \\ \hline Metric & Method & Stem & Leaf & Stem & Leaf & Stem & Leaf & Mean \\ Method of this paper & 80.28 & 80.71 & 92.25 & 86.18 & 77.99 & 81.75 & 83.19 \\  & FPS & 78.75 & 74.22 & 91.55 & 85.32 & 80.16 & 83.65 & 82.28 \\  & Random & 80.04 & 76.90 & 90.60 & 84.53 & 77.08 & 83.51 & 82.11 \\  & Voxel + FPS & 77.21 & 74.92 & 90.26 & 83.50 & 78.94 & 81.37 & 81.03 \\  & Method of this paper & 80.28 & 89.86 & 92.25 & 90.73 & 77.99 & 86.18 & 86.22 \\  & FPS & 78.75 & 82.90 & 91.55 & 90.67 & 80.16 & 87.70 & 85.29 \\  & Random & 80.04 & 85.46 & 90.60 & 89.90 & 77.08 & 87.88 & 85.16 \\  & Voxel + FPS & 77.21 & 84.09 & 90.26 & 89.01 & 78.94 & 85.73 & 84.21 \\  & Method of this paper & 58.85 & 80.35 & 76.00 & 82.03 & 87.50 & 73.79 & 76.42 \\  & FPS & 56.66 & 77.86 & 68.42 & 81.16 & 86.96 & 76.38 & 74.57 \\  & Random & 60.39 & 78.53 & 73.58 & 81.47 & 92.63 & 78.37 & 77.50 \\  & Voxel + FPS & 55.21 & 80.35 & 62.43 & 79.53 & 89.39 & 70.45 & 72.89 \\  & Method of this paper & 87.86 & 79.52 & 99.61 & 81.85 & 95.87 & 84.44 & 88.19 \\ mRec & FPS & 89.64 & 73.87 & 99.90 & 80.59 & 96.65 & 84.47 & 87.35 \\  & Random & 89.29 & 74.25 & 99.22 & 79.36 & 95.65 & 83.25 & 86.84 \\  & Voxel + FPS & 88.93 & 73.32 & 99.22 & 77.79 & 95.22 & 83.11 & 86.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of instance segmentation metrics for four downsampling methods (%). of the original point cloud in practical visualization, thereby providing strong support for the visual verification and manual calibration of downsampled point clouds. A combined quantitative and qualitative analysis indicates that the FFN-based patch downsampling strategy outperforms traditional methods in maintaining global uniformity, preserving semantic and instance boundaries, and balancing computational efficiency, significantly improving the performance of downstream semantic and instance segmentation networks. Therefore, this method offers a general downsampling solution that combines accuracy and efficiency for fine-grained tasks in the 3D point-cloud domain, especially for the segmentation of crop organs.