# Unbiased 3D Semantic Scene Graph Prediction in Point Cloud Using Deep Learning
## 5 Conclusions and Discussion  
This paper proposes a 3D scene graph prediction model for efficient environment perception, focusing more on predicting the semantic graph about the class of entity objects and their relationships. MP-DGCNN is proposed for more robust feature extraction, and ENA-GNN is introduced to infer complex relationships. A category grouping weighting strategy was attempted to solve the problem of the long-tailed distribution of datasets. Our model was validated on the 3 DSSG dataset and achieved state-of-the-art performance. The research in this paper contributes to various tasks related to understanding 3D scenes for indoor autonomous robotic applications.  
However, the FPS algorithm samples the point cloud and the multi-scale pyramidal network structure in the point feature extraction process. Although the point cloud features can be aggregated well, the time cost increases. More efficient sampling and point feature aggregation algorithms can be found in the future. Furthermore, due to the limitations of the available public datasets and the unbalanced distribution of the category annotations,  
Figure 12: Experimental results for each category of the scene graph classification (SGCls) task, with categories listed from left to right according to the statistical frequency of the samples. Comparisons are made using our method (GW + ME) and our method (GW + UME) with KSGPN (ME).  
Figure 13: Experimental results for each category of the predicate classification (PredCls) task, with categories ordered from left to right according to the statistical frequency of the samples. Comparisons are made using our method (GW + ME) and our method (GW + UME) with KSGPN (ME).  
the current approach relies, to some extent, on the dataset's completeness. It is currently limited to predicting indoor scenes and needs to be explored more deeply in more complex scenes, such as outdoor and dynamic scenes.  
In future work, considering the expansion of indoor and outdoor scenes and downstream application fields, future research can rely on the SLAM algorithms combined with real-time dynamic point cloud instance segmentation and 3D object detection, and other deep learning technologies. The ultimate expectation is to achieve dynamic predictions and generations of indoor and outdoor scene graphs. It is expected to be applied to robotic scene understanding tasks, thus extending the AR/VR domain and aiding the interactive perceptual behavior of intelligent agents within.  
Conceptualization, methodology and writing--original draft preparation, C.H.; data curation, C.H. and Y.W.; supervision, project management, fund acquisition and resources, S.Z. and H.L.; software and validation, C.H., B.D. and J.X.; investigation, C.H., X.Z., S.Z. and H.L.; visualization, writing--review and editing, C.H., H.L., Y.W. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the key project of the National Natural Science Foundation of China--\"Machine Map Theory and Modeling Method\", with support number 42130112.  
Not applicable.  
Data Availability Statement: Data can be obtained from the corresponding author upon reasonable request.  
The author would like to thank those who contributed to the model and dataset open sources. At the same time, we thank the editors and reviewers for their valuable comments.  
The authors declare no conflict of interest.