Reviewing 3D Object Detectors in the Context of High-Resolution 3+1D
  Radar
Patrick Palmer Martin Krueger Richard Altendorfer Ganesh Adam Torsten Bertram
Recent developments and the beginning market introduction of high-resolution
imaging 4D (3+1D) radar sensors have initialized deep learning-based radar
perception research. We investigate deep learning-based models operating on
radar point clouds for 3D object detection. 3D object detection on lidar point
cloud data is a mature area of 3D vision. Many different architectures have
been proposed, each with strengths and weaknesses. Due to similarities between
3D lidar point clouds and 3+1D radar point clouds, those existing 3D object
detectors are a natural basis to start deep learning-based 3D object detection
on radar data. Thus, the first step is to analyze the detection performance of
the existing models on the new data modality and evaluate them in depth. In
order to apply existing 3D point cloud object detectors developed for lidar
point clouds to the radar domain, they need to be adapted first. While some
detectors, such as PointPillars, have already been adapted to be applicable to
radar data, we have adapted others, e.g., Voxel R-CNN, SECOND, PointRCNN, and
PV-RCNN. To this end, we conduct a cross-model validation (evaluating a set of
models on one particular data set) as well as a cross-data set validation
(evaluating all models in the model set on several data sets). The
high-resolution radar data used are the View-of-Delft and Astyx data sets.
Finally, we evaluate several adaptations of the models and their training
procedures. We also discuss major factors influencing the detection performance
on radar data and propose possible solutions indicating potential future
research avenues.