  The perception of autonomous vehicles has to be efficient, robust, and
cost-effective. However, cameras are not robust against severe weather
conditions, lidar sensors are expensive, and the performance of radar-based
perception is still inferior to the others. Camera-radar fusion methods have
been proposed to address this issue, but these are constrained by the typical
sparsity of radar point clouds and often designed for radars without elevation
information. We propose a novel camera-radar fusion approach called Dual
Perspective Fusion Transformer (DPFT), designed to overcome these limitations.
Our method leverages lower-level radar data (the radar cube) instead of the
processed point clouds to preserve as much information as possible and employs
projections in both the camera and ground planes to effectively use radars with
elevation information and simplify the fusion with camera data. As a result,
DPFT has demonstrated state-of-the-art performance on the K-Radar dataset while
showing remarkable robustness against adverse weather conditions and
maintaining a low inference time. The code is made available as open-source
software under https://github.com/TUMFTM/DPFT.
