  Multimodal sensor fusion methods for 3D object detection have been
revolutionizing the autonomous driving research field. Nevertheless, most of
these methods heavily rely on dense LiDAR data and accurately calibrated
sensors which is often not the case in real-world scenarios. Data from LiDAR
and cameras often come misaligned due to the miscalibration, decalibration, or
different frequencies of the sensors. Additionally, some parts of the LiDAR
data may be occluded and parts of the data may be missing due to hardware
malfunction or weather conditions. This work presents a novel fusion step that
addresses data corruptions and makes sensor fusion for 3D object detection more
robust. Through extensive experiments, we demonstrate that our method performs
on par with state-of-the-art approaches on normal data and outperforms them on
misaligned data.
