# MpcNet: A Vision Transformer Network for Estimating Room Layouts from a Single RGB Panorama
## 4 Experimental Results and Discussion
### Quantitative Evaluation  
Our approach was evaluated on the three above-mentioned standard metrics: (1) 3D IoU, (2) corner error and (3) pixel error. The descriptions of these three metrics were introduced in Section 3.1, so they are not repeated in this section. The results of different approaches are summarized in Tables 1-3. The input resolution of HorizonNet and LayoutNet [27] was 512 \(\times\) 1024. Our approach achieved state-of-the-art performance and outperformed existing methods under all settings. The mean of the three indicators (3D IoU, corner error and pixel error) are presented after each experiment was repeated five times. The T-test was the two-sample heteroscedastic assumption for HorizonNet and MreNet. Figure 4: Convergence behavior of MreNet and HorizonNet. Figure 5: Ablation study for MrelNet. (**a**) is for 3_D IoL_, (**b**) is for corner error (\(CE\)) and (**c**) is the results of pixel error (\(PE\)). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **3D IoU (\%)** & **Corner Error (\%)** & **Pixel Error (\%)** \\ \hline \multicolumn{4}{c}{Training on PanoContext dataset} \\ \hline PanoContext & 67.51 & 1.57 & 4.49 \\ HorizonNet & 82.11 & **0.79** & 2.26 \\ MrelNet & **82.23** & **0.79** & **2.24** \\ \hline T-test & **0.002** & **0.0164** & **0.07** \\ \hline \multicolumn{4}{c}{Training on PanoContext + Stanford-2D3D datasets} \\ \hline HorizonNet & 83.40 & 0.72 & 2.03 \\ MrelNet & **84.08** & **0.67** & **1.98** \\ \hline T-test & **0.048** & **0.0217** & **0.007** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative results of cuboid layout estimation evaluated on the PanoContext [24] dataset. Our approach outperforms all listed methods under all settings. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **3D IoU (\%)** & **Corner Error (\%)** & **Pixel Error (\%)** \\ \hline HorizonNet & 75.43 & **0.94** & 3.21 \\ MrelNet & **75.55** & **0.94** & **3.18** \\ \hline T-test & **0.004** & **0.023** & **0.048** \\ \hline \multicolumn{4}{c}{Training on Stanford-2D3D dataset} \\ \hline HorizonNet & 79.57 & 0.75 & 2.44 \\ MrelNet & **79.89** & **0.70** & **2.38** \\ \hline T-test & **0.047** & **0.015** & **0.041** \\ \hline \multicolumn{4}{c}{Training on PanoContext + Stanford-2D3D datasets} \\ \hline HorizonNet & 83.86 & 0.65 & 2.10 \\ MrelNet & **84.11** & **0.62** & **2.06** \\ \hline T-test & **0.037** & **0.031** & **0.034** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative results of cuboid layout estimation evaluated on the Stanford-2D3D [25] dataset. Our approach outperforms all listed methods under all settings. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **3D IoU (\%)** & **Corner Error (\%)** & **Pixel Error (\%)** \\ \hline \multicolumn{4}{c}{Training on PanoContext + Stanford-2D3D datasets} \\ \hline HorizonNet & 83.76 & 0.67 & 2.07 \\ MrelNet & **84.09** & **0.64** & **2.03** \\ \hline T-test & **0.021** & **0.027** & **0.044** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative results of cuboid layout estimation evaluated on the Stanford-2D3D [25]+ PanoContext dataset [24]. Our approach outperforms all listed methods under all settings. the same rooms. The obtained layout estimation results are shown in Figure 6. It is obvious from the image that the layout estimation generated by the MreNet is more accurate in predicting the boundary of each plane, especially for the estimation of the location of the boundary between the ceiling and the wall. To further verify the correctness of this conclusion, we used multiple panoramic images for comparative experiments, and the results obtained are depicted in Figure 7 where the green lines are layout estimated by MreNet while the orange lines are for HorizonNet. It can be seen from the image that although both can perform relatively accurate layout estimation, MreNet can more accurately fit the boundary position of each plane, such as the position of the boundary between the ceiling and wall.