# Unbiased 3D Semantic Scene Graph Prediction in Point Cloud Using Deep Learning
## 4 Experimental Section
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**R@20**} & \multirow{2}{*}{**R@50**} & \multicolumn{2}{c}{**R@**} & \multirow{2}{*}{**ngR@20**} & \multirow{2}{*}{**ngR@50**} & \multirow{2}{*}{**ngR@100**} & \multicolumn{2}{c}{**mR@**} & \multicolumn{2}{c}{**mR@**} \\  & & & & & & & & & & **20** & **50** & **100** \\ \hline KERN [8] & 20.3 \(\pm\) 0.7 & 22.4 \(\pm\) 0.8 & 22.7 \(\pm\) 0.8 & 20.8 \(\pm\) 0.7 & 24.7 \(\pm\) 0.7 & 27.6 \(\pm\) 0.5 & 9.5\(\pm\) 1.1 & 11.5 \(\pm\) 1.2 & 11.9 \(\pm\) 0.9 \\ SGPN [11] & 27.0 \(\pm\) 0.1 & 28.8 \(\pm\) 0.1 & 29.0 \(\pm\) 0.1 & 28.2 \(\pm\) 0.1 & 32.6 \(\pm\) 0.1 & 35.3 \(\pm\) 0.1 & 19.7 \(\pm\) 0.1 & 22.6 \(\pm\) 0.6 & 23.1 \(\pm\) 0.5 \\ Schemata [9] & 27.4 \(\pm\) 0.3 & 29.2 \(\pm\) 0.4 & 29.4 \(\pm\) 0.4 & 28.8 \(\pm\) 0.1 & 33.5 \(\pm\) 0.3 & 36.3 \(\pm\) 0.2 & 23.8 \(\pm\) 1.2 & 27.0 \(\pm\) 0.2 & 27.2 \(\pm\) 0.2 \\ KSGPN [14] & 28.5 \(\pm\) 0.1 & 30.0 \(\pm\) 0.1 & 30.1 \(\pm\) 0.1 & 29.8 \(\pm\) 0.2 & 34.3 \(\pm\) 0.4 & 37.0 \(\pm\) 0.2 & 24.4 \(\pm\) 1.1 & 28.6 \(\pm\) 0.8 & 28.8 \(\pm\) 0.7 \\ KSGPN [14] & 32.3 \(\pm\) 0.1 & 33.7 \(\pm\) 0.2 & 33.9 \(\pm\) 0.1 & 34.1 \(\pm\) 0.1 & 38.5 \(\pm\) 0.3 & 41.1 \(\pm\) 0.3 & 26.9 \(\pm\) 0.6 & 29.5 \(\pm\) 0.5 & 30.1 \(\pm\) 0.3 \\ Ours & 34.2 \(\pm\) 0.2 & 35.6 \(\pm\) 0.1 & 35.7 \(\pm\) 0.2 & 36.4 \(\pm\) 0.5 & 41.1 \(\pm\) 0.4 & 44.1 \(\pm\) 0.3 & 26.8 \(\pm\) 0.5 & 29.3 \(\pm\) 0.6 & 29.8 \(\pm\) 0.2 \\ Ours & **35.8 \(\pm\) 0.1** & **37.1 \(\pm\) 0.2** & **37.2 \(\pm\) 0.2** & **38.2 \(\pm\) 0.2** & **42.6 \(\pm\) 0.2** & **45.4 \(\pm\) 0.1** & **31.1 \(\pm\) 0.3** & **33.8 \(\pm\) 0.5** & **33.9 \(\pm\) 0.4** \\ \hline \hline \end{tabular}  
* Remove UME and do not use a priori knowledge.  
\end{table} 
Table 1: Results of quantitative analysis of SGCIs for the scene graph classification task (in %, inclusive standard deviation and bolded for best performance). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**R@20**} & \multirow{2}{*}{**R@50**} & \multirow{2}{*}{**R@50**} & \multirow{2}{*}{**ngR@20**} & \multirow{2}{*}{**ngR@50**} & \multirow{2}{*}{**ngR@100**} & \multicolumn{2}{c}{**mR@**} & \multicolumn{2}{c}{**mR@**} \\  & & & & & & & & & 20 & **50** & **100** \\ \hline KERN [8] & 46.8 \(\pm\) 0.4 & 55.7 \(\pm\) 0.7 & 56.5 \(\pm\) 0.7 & 48.3 \(\pm\) 0.3 & 64.8 \(\pm\) 0.6 & 77.2 \(\pm\) 1.1 & 18.8 \(\pm\) 0.7 & 25.6 \(\pm\) 1.0 & 26.5 \(\pm\) 0.9 \\ SGPN [11] & 51.9 \(\pm\) 0.4 & 58.0 \(\pm\) 0.5 & 58.5 \(\pm\) 0.4 & 54.5 \(\pm\) 0.6 & 70.1 \(\pm\) 0.1 & 82.4 \(\pm\) 0.2 & 32.1 \(\pm\) 0.4 & 38.4 \(\pm\) 0.6 & 38.9 \(\pm\) 0.6 \\ Schemata [9] & 48.7 \(\pm\) 0.4 & 58.2 \(\pm\) 0.7 & 59.1 \(\pm\) 0.6 & 49.6 \(\pm\) 0.2 & 67.1 \(\pm\) 0.3 & 80.2 \(\pm\) 0.9 & 35.2 \(\pm\) 0.8 & 42.6 \(\pm\) 0.5 & 43.3 \(\pm\) 0.5 \\ KSGPN [14] & 52.9 \(\pm\) 0.4 & 59.8 \(\pm\) 0.5 & 54.9 \(\pm\) 0.4 & 71.6 \(\pm\) 0.5 & 82.4 \(\pm\) 0.8 & 35.3 \(\pm\) 1.1 & 41.0 \(\pm\) 0.7 & 41.5 \(\pm\) 1.0 \\ KSGPN [14] & 59.3 \(\pm\) 0.4 & 65.0 \(\pm\) 0.4 & 65.3 \(\pm\) 0.4 & 62.2 \(\pm\) 0.5 & 78.4 \(\pm\) 0.4 & 88.3 \(\pm\) 0.2 & 56.6 \(\pm\) 1.1 & 63.5 \(\pm\) 0.1 & 63.8 \(\pm\) 0.1 \\ KSGPN [14] & 62.6 \(\pm  
The visualized qualitative results of the scene graph classification (SGCls) are shown in Figure 9. Compared with the baseline model, KSGPN, our model outperforms the baseline model regarding object recognition and relationship inference, such as \"wall-door\" and \"tv-window\", which are confusingly similar objects. Our model can also infer the correct relationship between some isolated entities, which do not exist with other entities in the ground-truth labels, and the surrounding objects. Table 2 shows the results of the predicate classification task. In the predicate classification task, the category for the object is known information; therefore, improving the prediction effect is mainly related to the relational inference network and the use of prior knowledge.