# Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation
## 2 Related Work
### Radar Point Cloud Processing  
Radar signals can be represented in a similar data structure as lidar data. However, due to the greater sparsity and the lack of available radar data for algorithm development, the development in this area is less evolved. Many advances in the radar field are accomplished by industry research with direct access to labeled radar data. Recently, lidar processing techniques are transferred to the radar domain, taking into account the aforementioned differences between the two types of data.  
[PERSON] [17] uses a two-step approach to classify radar objects. A DBSCAN algorithm [18] is used to cluster the radar detections. In a second step, features are generated for these clusters and classified by a random forest or a simple LSTM cell. The LSTM network outperformed the random forest approach. Difficulties with generating adequate training data for the LSTM network are mentioned. Furthermore, the drawbacks of the two-step approach and the manual corrections for the clustering algorithm are mentioned.  
Another work [19] from the same group abstains from using a two-step clustering approach. Instead, they accumulate radar data over several time steps in a grid map. They create patches of \(8\times 8\) m, which are classified with a deep neural network approach. They train one-vs-all classifiers in a static environment to simplify the use case for the sparse radar data. The approach is evaluated on a proprietary data set.  
[PERSON] [20] is the first to apply a pointnet-based approach to automotive radar data for semantic segmentation. They argue that due to the lower point cloud density grid-based discretization is not feasible for radar processing, as most grid cells would remain empty. Their approach segments different moving object classes against a static background class. It is important to note that moving objects cannot simply be distinguished from the environment by taking into account the doppler-measured velocity of the radar. Effects of an imperfect time synchronization, multi-path reflections and general noise induce non-zero doppler measurements for static locations. Furthermore, they stress the importance of taking into account the time domain for radar data processing.  
A recent work by [PERSON] et al. [21] gives a comprehensive overview of a segmentation approach for both static and dynamic objects. They apply a convolutional encoder-decoder network on an extended grid map to classify static points. Moving points are segmented with a pointnet-based approach. The network integrates a recurrent structure by associating point features of past time steps with current point features. The time dependency is limited by the removal of old points from their memory point cloud to keep it at a fixed size. The approach is evaluated on a proprietary data set.  
[PERSON] [22] performs radar point segmentation by using both the low-level radar cube and the processed radar point cloud data level. They evaluate their approach on the same proprietary data as [17] and show that they can set a new benchmark score by taking into account the additional data source.  
[PERSON] [23] also uses a pointnet approach. Their network first segments patches of the environment for possible objects. Consecutively, the network processes the segmented object points to regress the bounding box dimensions for object detection. As with the other works, they evaluate their approach on a proprietary data set.