  Monocular 3D object detection is an inherently ill-posed problem, as it is
challenging to predict accurate 3D localization from a single image. Existing
monocular 3D detection knowledge distillation methods usually project the LiDAR
onto the image plane and train the teacher network accordingly. Transferring
LiDAR-based model knowledge to RGB-based models is more complex, so a general
distillation strategy is needed. To alleviate cross-modal prob-lem, we propose
MonoSKD, a novel Knowledge Distillation framework for Monocular 3D detection
based on Spearman correlation coefficient, to learn the relative correlation
between cross-modal features. Considering the large gap between these features,
strict alignment of features may mislead the training, so we propose a looser
Spearman loss. Furthermore, by selecting appropriate distillation locations and
removing redundant modules, our scheme saves more GPU resources and trains
faster than existing methods. Extensive experiments are performed to verify the
effectiveness of our framework on the challenging KITTI 3D object detection
benchmark. Our method achieves state-of-the-art performance until submission
with no additional inference computational cost. Our codes are available at
https://github.com/Senwang98/MonoSKD
