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.

通过使用 transformers 在多个传感器的多个抽象级别上映射特征，我们提出了首个无需校准的 3D 物体检测方法，消除了复杂和昂贵的校准过程，不仅在 BEV mAP 上优于单模态设置 14.1％，还表明 transformer 确实学习到了传感器映射。我们希望通过展示传感器融合不需要校准来激励其他研究者，同时得到的方法对旋转和平移变化具有很强的鲁棒性。