Learned Fusion: 3D Object Detection using Calibration-Free Transformer
  Feature Fusion
Michael Fürst Rahul Jakkamsetty René Schuster Didier Stricker
The state of the art in 3D object detection using sensor fusion heavily
relies on calibration quality, which is difficult to maintain in large scale
deployment outside a lab environment. We present the first calibration-free
approach for 3D object detection. Thus, eliminating the need for complex and
costly calibration procedures. Our approach uses transformers to map the
features between multiple views of different sensors at multiple abstraction
levels. In an extensive evaluation for object detection, we not only show that
our approach outperforms single modal setups by 14.1% in BEV mAP, but also that
the transformer indeed learns mapping. By showing calibration is not necessary
for sensor fusion, we hope to motivate other researchers following the
direction of calibration-free fusion. Additionally, resulting approaches have a
substantial resilience against rotation and translation changes.