# Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation
## 5 Discussion
### Data
The chosen cut-off value thereby likely filters out many pedestrian detections that cannot be used for training even when the additional filter settings in the data set API are deactivated. By visualizing the data set, we found that not only pedestrian detections are affected by this, but also passenger vehicle are often not detected in the data set. Figure 12 shows a vehicle in line-of-sight direction from the radar sensor. However, no radar points are present inside or near the ground-truth bounding box. Figure 11: A bus approaching the sensor vehicle in BEV projection. The black color of the ground-truth points and bounding box indicates a static vehicle, despite the bus clearly approaching as indicated by its measured relative velocity. NuScenes sample token: 87a927d7e61345d3a098 eb66908 bddcb. While introducing the _vllh_-tolerance, we mentioned that the ground-truth labels are not always accurate regarding the radar returns. Radar reflections seem to originate from outside the ground-truth bounding boxes. The _vllh_-tolerance can mitigate this problem but not completely erase it. No matter the choice of a bounding box tolerance, there will always be returns that lie outside of it and will either \"confuse\" the learning algorithm or count negatively towards the segmentation score, while seeming to be classified correctly intuitively. The biggest artificial barrier for successful training that we encountered is the absence of labels for objects that are clearly visible in radar view. Figure 13 shows several moving vehicles from a BEV perspective. Although the radar points on the right are correctly labeled as moving vehicles, the vehicles further to the left in the scene, on both sides of the road, are not labeled. Their presence can be estimated from the point clusters with non-zero velocity. The nuScenes labels are created from lidar view. The resulting number of samples with missing labels for radar data limits the data set quality. The nuScenes data set is a potent data set for measuring the quality of lidar and camera detection algorithms. The effects mentioned decrease the data quality for the training of radar algorithms significantly. Nonetheless, it is an important contribution to the radar community as it remains the only usable 3D-labeled source of radar point cloud data available to the public. Although it can serve as a comparison benchmark for radar detection algorithms, it cannot measure the absolute perfo  
Figure 12: Vehicle in line-of-sight direction from the radar sensor. Despite its exposed position, it is not detected with the sensor settings used. (**a**) The vehicle in camera view in front of the sensor vehicle. (**b**) BEV: Zoom onto the GT bounding box in blue and the radar data in the surroundings. No radar return originates from the vehicle. NuScenes sample token: bbba31d9e33f44 bfd4196 bf699980. Figure 13: Moving vehicles ground-truth color-coded in blue. Ground-truth bounding boxes are not labeled for four vehicles in the sensor field of view, though they are clearly distinguishable by their relative velocity. NuScenes sample token: e2c7c91b4 ea2462090d866539 ff6b9e5. when the algorithms are applied to a real-world use case with a comparable radar module. Slight corrections, such as using the _wll_-tolerance or filtering for samples with high data density, can be performed to increase the data quality. The creation of a production quality data set, however, would require manual filtering of the instances and possibly even relabeling to create a generally applicable sub data set. This kind of complete revision of the labels seems unreasonable, considering the low raw radar data quality in the data set. We therefore see a great demand for the release of a data set tailored for radar object detection. This would make it easier to compare different radar processing approaches. The absolute model performance that is measured on private data sets can hardly serve as a metric for comparison due to the huge impact of the underlying data set on the segmentation result.