# Fast 3D Semantic Mapping in Road Scenes +
## 5 Experiments and Results
### Quantitative Results  
For the quantitative performance of our approach, we focus on the 2D semantic segmentation and the runtime of the entire system, since the 3D reconstruction mainly depends on the SLAM module. _Semantic Segmentation:_ Table 3 shows the quantitative results of 2D semantic segmentation based on different DeepLab-v3+ models on the KITTI datasets. We evaluate these models by the mean intersection/union (mIOU) score, the model size, and the computational runtime. The mIOU score is defined as  
\[\text{mIOU}=\frac{1}{|\mathcal{L}|}\sum_{i=1}^{|\mathcal{L}|}\text{TP}_{i}/( \text{TP}_{i}+\text{FP}_{i}+\text{FN}_{i}) \tag{21}\]  
in terms of the True/False Positives/Negatives for a given class \(i\). We do not resize the image to evaluate the models here. Whereas, for the 3D semantic mapping process, we need to half resize the input images in order to make a trade-off between accuracy and computational speed. Figure 8: Comparison between the before CRF processing and the after CRF processing from different views of the 3D semantic map on the KITTI _odometry_03_. Figure 9: A qualitative result of 3D semantic mapping on the KITTI _odometry_05_. During the training process, these models are initialized with the checkpoints pre-trained from various datasets including ImageNet [43] and MS-COCO [44]. In the training step on the Cityscapes dataset, we directly use the ImageNet-pretrained checkpoints as the initialization. Note we employ the _MobileNet_v2_ based model which has been pre-trained on MS-COCO dataset, and the _Xception_71_ based model has been pre-trained on both ImageNet and MS-COCO datasets. These pre-trained models can be accessed from the github ([[https://github.com/tensorflow/models/blob/master/research/deeplab/g3](https://github.com/tensorflow/models/blob/master/research/deeplab/g3) doc/model_zoo.md]([https://github.com/tensorflow/models/blob/master/research/deeplab/g3](https://github.com/tensorflow/models/blob/master/research/deeplab/g3) doc/model_zoo.md)). Then we fine-tune the models on the KITTI dataset by using the pre-trained Cityscapes model. The _Xception_71_ based model performs the best mIOU performance but a rather slow computational speed. The _MobileNet_v2_ based model has a moderate mIOU, the smallest file size and the fastest speed. Note the _MobileNet_v2_ based model does not employ ASPP and decoder modules for fast computation. Considering the balance between computational speed and accuracy, we choose the _MobileNet_v2_ based model to carry out the 2D semantic segmentation in our approach. Table 4 shows the performance of the _MobileNet_v2_ based model on the VAL/TEST split of the KITTI dataset. We also make the test regarding to the effect of pre-training on the Cityscapes dataset. In Table 5, the salience has been illustrated on training the _Xception_65_ and _MobileNet_v2_ models. The Cityscapes pre-trained models could greatly improve the performance of 2D semantic segmentation on the KITTI dataset. Note that towards the 3D semantic mapping, since we use a novel monocular 3D mapping different from the other related work, it is not easy to make quantitative comparison here. [PERSON] et al. 's work [28] proposes a joint semantic segmentation and 3D reconstruction from monocular video, but it is an offline approach with different 3D representation in the form of a 3D volumetric semantic and occupancy map.