# Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation
## 5 Discussion
### Model Critique  
Due to the low information content in the underlying radar data, model training requires a careful calibration. Although higher segmentation scores could not be achieved, slight variations in learning rate could result in models barely outperforming a guessing model. The low radar density is partly the result of the radar processing itself. In the range-velocity dimension of the radar cube, moving objects are more differentiable from the  
Figure 8: Classification of a moving vehicle. Ground-truth bounding boxes and radar classifications are color-coded. Blue: moving vehicle. Black: rest. The segmentation in (**a**) is more precise than the segmentation in (**b**) due to the inclusion of velocity features. NuScenes sample token: 9a7d5c8 adce4 ae68 ca26 ad5b593366. static background than static vehicles. Consequently, many labeled static vehicle bounding boxes do not include any or only a few radar points. This is one reason the moving vehicle segmentation performed better than the static case. The presented LSTM models, in comparison to [21], do not forget old points once an intermediate memory point cloud is full. The data is added or forgotten by the memory point cloud according to the LSTM structure. We assume this to be an advantage of the presented architecture. However, due to the different underlying data source, a direct comparison of the models is not possible here. We experimented with different functions to associate consecutive radar point clouds in the KPLSTM cell. In general, we would expect learning methods to provide the most accurate data association capability on noisy radar data. However, nearest neighbor sampling outperformed learning-based methods on the presented data set. We assume that the learning of an appropriate association function is not feasible due to the corruption of the radar data in the data set. A detailed study of different learning methods for data association is postponed until an appropriate data is available. Radar data comes with a class imbalance towards the background class. Most radar reflections originate from non-traffic participants, which makes a high accuracy score possible through the classification of all points as background. The class-weighted focal loss function helps mitigate this problem, by assigning strongly misclassified examples of the minority classes with a higher weight during backpropagation. At this stage, it is difficult to pinpoint whether the model or the data has the greatest effect on drawbacks of the model performance. To further study the discriminative content in the data set, we overfit the KPConv model to two select scenes from the nuScenes data set. When training a model to segment static and moving vehicles alike, a perfect score is not achieved within 10,000 epochs of training with the same scene. Figure 9a shows the resulting confusion matrix for a _All Vehicle_ configuration. Figure 10 shows an extract from this scene. The radar returns of the vehicles have a high similarity to points returned from the background, making even overfitting to the data a challenging task. For moving vehicle segmentation, we were able to achieve a perfect segmentation for a scene of a vehicle following scenario. Figure 9b,c shows the confusion matrices for this scene with two different compensations methods for the spatial misalignment. Please note that it was only possible to achieve a perfect score when a _vhl_-tolerance was applied in Figure 9c. When a _vhl_-factor was used in Figure 9b, not all points from the moving object class were correctly associated with the ground-truth bounding box. The model learned to segment these points to the moving vehicle class due to its similarity to points inside the ground-truth box; however, they count negatively towards the metric score. Figure 9: Overfitting confusion matrices. (**a**) Perfect overfitting is not achieved for the _All Vehicle_ configuration scene on the left. (**b**) The _Moving Vehicle_ data scene is not perfectly segmented when a _vhl_-factor is used.