# Radar Voxel Fusion for 3D Object Detection
## 3 Methodology  
In the following, we list the main conclusions from the state of the art for our work:  
* _Input representation_: The input representation of the sensor data dictates which subsequent processing techniques can be applied. Pointnet-based methods are beneficial when dealing with sparse unordered point cloud data. For more dense--but still sparse--point clouds, such as the fusion of several lidar or radar sensors, sparse voxel grid structures achieve more favorable results in the object detection literature. Therefore, we adopt a voxel-based input structure for our data. As many of the voxels remain empty in the 3D grid, we apply sparse convolutional operations [12] for greater efficiency.
* _Distance Threshold_: Anchor-based detection heads predominately use an IoU-based matching algorithm to identify positive anchors. However, [PERSON] [21] has shown that this choice might lead to association of distant anchors for certain bounding box configurations. We argue that both IoU- and distance-based matching thresholds should be considered to facilitate the learning process. The distance-based threshold alone might not be a good metric when considering rotated bounding boxes with a small overlapping area. Our network therefore considers both thresholds to match the anchor boxes.
* _Fusion Level_: The data from different sensors and modalities can be fused at different abstraction levels. Recently, a rising number of papers perform early or feature fusion to be able to facilitate all available data for object detection simultaneously. Nonetheless, the state of the art in object detection is still achieved by considering only lidar data. Due to its resolution and precision advantage from a hardware perspective, software processing methods cannot compensate for the missing information in the input data of the additional sensors. Still, there are use cases where the lidar sensor alone is not sufficient. Inclement weather, such as fog, decreases the lidarand camera data quality [26] significantly. The radar data, however, is only slightly affected by the change in environmental conditions. Furthermore, interference effects of different lidar modules might decrease the detection performance under certain conditions [27; 28]. A drawback of early fusion algorithms is that temporal synchronized data recording for all sensors needs to be available. However, none of the publicly available data sets provide such data for all three sensor modalities. The authors of [7] discuss the publicly available data quality for radar sensors in more detail. Despite the lack of synchronized data, this study uses an early fusion scheme, as in similar works, spatio-temporal synchronization errors are treated as noise and compensated during the learning process of the fusion network. In contrast to recent papers, where some initial processing is applied before fusing the data, we present a direct early fusion to enable the network to learn optimal combined features for the input data. The early fusion can make use of the complementary sensor information provided by radar, camera and lidar sensors--before any data compression by sensor-individual processing is performed.