# Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation
## 3 Methodology  
Taking into account the related work above, we emphasize the importance of the following points for the development of a semantic segmentation network for radar data:* _Data Availability_: In contrast to lidar research, where a vast amount of labeled data is publicly available, the access to radar data is more restricted. Accurately labeled data is the basis for any successful supervised learning model, which means that reduced data availability could be one reason for the smaller amount of research work in this area. A more detailed view of radar data available to the public is given in Section 5.2. * _Data Level_: Due to the sensor principle of the radar, a variety of intermediate processing stages occur before the raw receive signal is converted into object proposals. The unprocessed low-level raw antenna signals theoretically contain the most information; however, there are no methods of labeling data on this abstraction level for object categories. Additionally, depending on the antenna characteristics and configuration, the data would differ a lot for different radar types, which would require a specific learning approach for different sensor hardware designs. Consequently, to the best of our knowledge, no one-step learning approach exists for classifying objects directly on the raw antenna signals. One can perform object detection at the radar cube level or on any of its 2D projections, e.g., range-azimuth plot. However, data labeling of this 3D representation (2D location + velocity) would still be a tedious task. [22] performs the labeling by extracting data from the cube at manually labeled point target locations. However, in this way the label quality can only approximate the ground-truth, as the point target might not include all raw signals that originate from an object. Furthermore, the point targets only represent a compression of the radar signal, which means that the point signal cannot be re-projected precisely on the cube-level signal. To our knowledge, no full 3D cube-level radar data set has been released to the public. However, due to the high informativeness and the similarity to image data, which is heavily processed with learning-based techniques, the cube-level data is an interesting use case for future deep-learning research on radar data. Point level data is the most explored data level for deep learning on radar data. It represents a compromise between data informativeness and data amount. Due to the similarity to lidar point cloud data, algorithms can be transferred from the lidar domain to the radar domain. This work operates on point level radar data. We benchmark our models on the public nuScenes data set [2], which includes labeled point cloud radar data. * _Data Sparsity_: For lidar data processing, both grid and point-based approaches, are used in the state of the art. Similar to [20], we argue that for radar data, a point-based approach is more feasible than the intermediate grid representation as most of the grid cells would remain empty. For fusion approaches of radar data with denser data such as images or lidar both grid-based and point-based approaches [24; 25; 26], might be suitable. This work presents a point-based approach. To the best of our knowledge, we are the first to adapt the KPConv architecture for radar point cloud data. * _Time Dependency_: Due to the low spatial resolution of radar sensors, the time domain plays an important role in radar processing. Current production radar systems track point level detections over several timeframes before recognizing a track as an object. This decreases the number of false positive object detections due to clutter. These false positives would otherwise be harmful to driver assistant systems such as adaptive cruise control (ACC) or emergency brake assist. In the literature, approaches that use the time domain in learning models such as simple LSTM cells or memory point clouds have proven successful for radar data processing.