# Semi-Supervised Semantic Segmentation Network for Point Clouds Based on 3D Shape
## 5 Conclusions  
In this paper, we present SBSNet, a network model designed for 3D point cloud classification and semantic segmentation tasks. SBSNet uses multiple point cloud geometric features grouped based on shape information and encoded by neural networks. The shape information is fused using an attention mechanism to capture salient local features that form feature descriptors to achieve semantic segmentation and classification.  
To validate the proposed model, we compared it with classical and recent deep learning-based methods using the classification dataset ModelNet40 and the segmentation dataset ShapeNet. In the ModelNet40 dataset, SBSNet had 90.6% MAcc, which was higher by 4.4%, 2.5%, 0.3%, and 0.2% than that of PointNet, PointCNN, GCN3D, and MANet, respectively. Moreover, SBSNet achieved 93.1% OA, which was 2.4%, 0.2%, and 0.2% higher than that of PointNet++, DGCNN, and DTNet, respectively. Furthermore, its overall MIoU in the ShapeNet dataset was higher by 1.6%, 0.2%, 11.8% and 0.1% than that of PointNet, PointNet++, SRINet, and Pointseq2 seq, respectively. At the same time, in many categories, SBSNet achieved the highest MIoU. Our experimental results also confirmed  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**512 Points** & **1024 Points** & **2048 Points** & **2860 Points** \\ \hline \hline \end{tabular}
\end{table}
Table 6: SBSNet resolution robustness results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**GT** & **Proposed** & **GT** & **Proposed** \\ \hline \hline \end{tabular}
\end{table}
Table 4: SBSNet segmentation visualization results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Inputted Points** & **MIoU (\%)** \\ \hline
512 & 76.13 \\
1024 & 81.22 \\
2048 & 85.35 \\
2560 & 85.25 \\ \hline \hline \end{tabular}
\end{table}
Table 5: SBSNet resolution robustness results.  
the effectiveness of the proposed model, which achieved point cloud data segmentation and classification at a level comparable to that of many state-of-the-art models.  
Although SBSNet can effectively capture the relationship between geometric features and significant local features of point cloud data, its training time was slightly longer than other networks according to the training time experiment. However, our method was superior to other networks in both classification and individual experiments. In the future, we will explore better and smaller networks to ensure accuracy and reduce the training time.  
Conceptualization, L.Z.; methodology, L.Z. and K.Z.; software, L.Z.; validation, L.Z. and K.Z.; formal analysis, L.Z.; investigation, L.Z.; resources, K.Z.; data curation, L.Z.; writing--original draft preparation, L.Z.; writing--review and editing, L.Z.; visualization, L.Z.; supervision, K.Z.; project administration, K.Z.; funding acquisition, K.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The ShapeNet Dataset and ModelNet40 Dataset used for this study can be accessed at [[https://shapenet.org](https://shapenet.org)]([https://shapenet.org](https://shapenet.org)) and [[https://modelnet.cs.princeton.edu](https://modelnet.cs.princeton.edu)]([https://modelnet.cs.princeton.edu](https://modelnet.cs.princeton.edu)).  
The authors declare no conflict of interest.