# Fast 3D Semantic Mapping in Road Scenes +
## 5 Experiments and Results
### Quantitative Results
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c} \hline \hline
**Dataset** & **Encoder** & **Crop Size** & **mIOU[0.5:0.25:1.75]** & **Pb Size (MB)** & **Runtime (ms)** & **I** & **M** & **C** \\ \hline \hline \multirow{8}{*}{Cityscapes} & ResNet\_50 & 769 & 63.9 & 107.8 & - & \(\surd\) & & \\  & ResNet\_101 & 769 & 69.9 & 184.1 & - & \(\surd\) & & \\  & Xception\_41 & 769 & 68.5 & 113.4 & - & \(\surd\) & & \\  & Xception\_65 & 769 & 78.7 & 165.7 & 1800 & \(\surd\) & & \\  & Xception\_71 & 769 & 80.2 & 167.9 & 2000 & \(\surd\) & \(\surd\) & \\  & MobileNet\_v2 & 513 & 70.7 & 8.8 & 400 & & \(\surd\) & \\  & MobileNet\_v2 & 769 & 70.9 & 8.8 & 400 & & \(\surd\) & \\ \hline \hline \multirow{8}{*}{KITTI} & ResNet\_50 & 769 & 51.4 & 107.8 & 120 & \(\surd\) & \(\surd\) & \(\surd\) \\  & ResNet\_101 & 769 & 57.1 & 184.1 & 140 & \(\surd\) & \(\surd\) & \(\surd\) \\  & Xception\_41 & 769 & 54.2 & 113.4 & 140 & \(\surd\) & \(\surd\) & \(\surd\) \\  & Xception\_65 & 769 & 64.8 & 165.6 & 160 & \(\surd\) & \(\surd\) & \(\surd\) \\  & Xception\_71 & 769 & 66.2 & 167.9 & 170 & \(\surd\) & \(\surd\) & \(\surd\) \\  & MobileNet\_v2 & 513 & 57.7 & 8.8 & 80 & & \(\surd\) & \(\surd\) \\  & MobileNet\_v2 & 769 & 60.7 & 8.8 & 80 & & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative results of various encoder parts of DeepLab-v3+ on the Cityscapes and the KITTI. I: ImageNet, M: MS-COCO, C: Cityscapes. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c} \hline \hline
**Dataset** & **Encoder** & **Crop Size** & **mIOU[0.5:0.25:1.75]** & **Pb Size (MB)** & **Runtime (ms)** & **I** & **M** & **C** \\ \hline \hline \multirow{8}{*}{Cityscapes} & ResNet\_50 & 769 & 63.9 & 107.8 & - & \(\surd\) & & \\  & ResNet\_101 & 769 & 69.9 & 184.1 & - & \(\surd\) & & \\  & Xception\_41 & 769 & 68.5 & 113.4 & - & \(\surd\) & & \\  & Xception\_65 & 769 & 78.7 & 165.7 & 1800 & \(\surd\) & & \\  & Xception\_71 & 769 & 80.2 & 167.9 & 2000 & \(\surd\) & \(\surd\) & \\  & MobileNet\_v2 & 513 & 70.7 & 8.8 & 400 & & \(\surd\) & \\  & MobileNet\_v2 & 769 & 70.9 & 8.8 & 400 & & \(\surd\) & \\ \hline \hline \multirow{8}{*}{KITTITI} & ResNet\_50 & 769 & 51.4 & 107.8 & 120 & \(\surd\) & \(\surd\) & \(\surd\) \\  & ResNet\_101 & 769 & 57.1 & 184.1 & 140 & \(\surd\) & \(\surd\) & \(\surd\) \\  & Xception\_41 & 769 & 54.2 & 113.4 & 140 & \(\surd\) & \(\surd\) & \(\surd\) \\  & Xception\_65 & 769 & 64.8 & 165.6 & 160 & \(\surd\) & \(\surd\) & \(\surd\) \\  & Xception\_71 & 769 & 66.2 & 167.9 & 170 & \(\surd\) & \(\surd\) & \(\surd\) \\  & MobileNet\_v2 & 513 & 57.7 & 8.8 & 80 & & \(\surd\) & \(\surd\) \\  & MobileNet\_v2 & 769 & 60.7 & 8.8 & 80 & & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative results of various encoder parts of DeepLab-v3+ on the Cityscapes and the KITTI. I: ImageNet, M: MS-COCO, C: Cityscapes. _Runtime and Storage:_ As shown in Table 6, the SLAM module in our system runs about 40 ms on average to process each frame, extract the keyframes and update the map. The semantic segmentation process requires about 100 ms to infer 2D semantic information parallel upon the keyframes, and the incremental fusion process needs 50 ms on average. In the experiments, the SLAM process at least selects a keyframe every 4 or 5 frames. It keeps enough timing for the 2D semantic segmentation and the incremental fusion during the 3D semantic mapping. Thus, our approach could run in real-time. Moreover, considering the speed of moving platform, in case of the speed of 60 km/h, the semantic segmentation process on selected keyframes corresponds to a distance about 2 meters, which is not too sparse for an urban scene. The lower part of this table shows the ranges of the CRF timing with different configurations due to the different size of point clouds when testing various sequences in the experiments. The CRF update runs offline due to slow inference speed on the CPU. Thus, it is only applied once at the end of the sequence.