# Boundary-Inner Disentanglement Enhanced Learning for Point Cloud Semantic Segmentation
## 4 Experimental Results and Discussion
### Toronto-3D Outdoor Scene Segmentation  
This paper also demonstrates the generalizability of BIDEL using an outdoor dataset, Toronto-3D [48]. This is a large-scale urban outdoor point cloud dataset acquired by the MLS system in Toronto, Canada, covering about 1 km of point clouds and consisting of about 78.3 million points belonging to one of eight classes, such as road markings and cars. It covers four blocks. The L002 scene was selected for validation and testing, whereas the other scenes were selected for training. This dataset is labeled inaccurately in some areas. For example, objects that should be utility lines are labeled as buildings (Figure 7a) or trees (Figure 7b), while objects that should be poles are labeled as natural (Figure 7c). Although each point provides rich attributes such as xyz coordinates, rgb colors, intensity, GPS time, scan angle rank, and class label, this experiment only used the xyz and rgb attributes, following the same settings as used for S3 DIS. There were some challenges when performing the semantic segmentation task: (1) objects belonging to the pole/natural/utility line categories often overlapped with each other; (2) road markings were small and narrow objects. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **mIoU** & **OA** & **mACC** & **Coil.** & **Floor** & **Wall** & **Beam** & **Col.** & **Wind.** & **Door** & **Table** & **Chair** & **Sofa** & **Book.** & **Board** & **Clut.** \\ \hline PointNet [33] & 41.1 & - & 49.0 & 88.8 & 97.3 & 69.8 & 0.1 & 3.9 & 46.3 & 10.8 & 59.0 & 52.6 & 5.9 & 40.3 & 26.4 & 33.2 \\ SegCloud [50] & 48.9 & - & 57.4 & 90.1 & 96.1 & 69.9 & 0.0 & 18.4 & 38.4 & 23.1 & 70.4 & 75.9 & 40.9 & 58.4 & 13.0 & 41.6 \\ PointCNN [29] & 57.3 & 85.9 & 63.9 & 92.3 & 98.2 & 79.4 & 0.0 & 17.6 & 22.8 & 62.1 & 74.4 & 80.6 & 31.7 & 66.7 & 62.1 & 56.7 \\ SPG [51] & 58.0 & 86.4 & 66.5 & 89.4 & 96.9 & 78.1 & 0.0 & **42.8** & 48.9 & 61.6 & 84.7 & 75.4 & 69.8 & 52.6 & 2.1 & 52.2 \\ GAC [40] & 62.9 & 87.8 & - & 92.3 & 98.3 & 82.0 & 0.0 & 20.4 & 59.0 & 40.9 & 85.8 & 78.6 & 70.8 & 61.7 & 74.7 & 52.8 \\ PCT [2] & 61.3 & - & 67.7 & 92.5 & 98.4 & 80.6 & 0.0 & 19.4 & 61.6 & 48.0 & 76.6 & 85.2 & 46.2 & 67.7 & 67.9 & 52.3 \\ IAFNet [6] & 64.6 & 88.4 & 70.4 & 91.4 & 98.6 & 81.8 & 0.0 & 34.9 & 62.0 & 54.7 & 97.7 & 86.9 & 49.9 & 72.4 & **74.8** & 52.1 \\ JSENet [7] & **67.7** & - & 93.8 & 97.0 & 83.0 & 0.0 & 23.2 & 61.3 & 71.6 & **89.9** & 79.8 & 75.6 & 72.3 & 72.7 & 60.4 \\ PushBoundary [7] & 67.1 & **89.7** & - & 94.0 & 97.9 & 82.6 & 0.0 & 23.3 & 56.6 & **75.4** & 80.1 & 91.1 & 75.7 & 74.4 & 62.3 & 59.1 \\ CBL [12] & 65.3 & 87.5 & **74.5** & 92.2 & 97.7 & 81.0 & 0.0 & 36.8 & 61.0 & 39.4 & 78.1 & 88.1 & **81.4** & 71.5 & 68.7 & 52.6 \\ \hline KPConv [31] & 66.2 & - & - & 94.8 & 98.4 & 82.9 & 0.0 & 18.0 & 53.4 & 67.1 & 83.0 & **91.4** & 63.8 & 75.5 & 71.6 & 60.8 \\ +BIDEL\({}^{*}\) & 67.0 & - & - & **94.9** & 98.5 & **83.3** & 0.0 & 21.4 & 54.9 & 68.5 & 83.1 & 91.2 & 66.0 & **75.7** & 71.8 & **61.1** \\ \hline RandLA-Net [28] & 62.7 & 87.5 & 71.1 & 92.3 & 97.8 & 80.7 & 0.0 & 19.5 & 59.2 & 46.8 & 78.0 & 85.7 & 63.2 & 70.9 & 68.0 & 53.0 \\ +BIDEL\({}^{*}\) & 64.7 & 88.0 & 73.6 & 93.0 & 96.1 & 81.5 & 0.0 & 28.8 & **62.7** & 43.9 & 74.0 & 87.0 & 72.6 & 71.9 & 73.7 & 55.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative results on S3 DIS Area 5. The red font denotes obvious better results (greater than 1%) than baseline. **Bold** font denotes the best result among all methods. \({}^{*}\) These methods consider boundaries. Table 4 summarizes the evaluation results and quantitative comparisons. BIDEL showed a slight improvement over the baselines, with obvious gains in the road marking (+4.5%) and car (+1.9%) categories based on RandLA-Net. With BIDEL, KPConv achieved the leading performance of 97.9% for roads, 76.3% for road markings, 82.6% for poles, and 95.1% for cars. Figure 8 visualizes the segmentation results. It is evident that the improved areas aligned with the boundary contours.