  Leveraging LiDAR-based detectors or real LiDAR point data to guide monocular
3D detection has brought significant improvement, e.g., Pseudo-LiDAR methods.
However, the existing methods usually apply non-end-to-end training strategies
and insufficiently leverage the LiDAR information, where the rich potential of
the LiDAR data has not been well exploited. In this paper, we propose the
Cross-Modality Knowledge Distillation (CMKD) network for monocular 3D detection
to efficiently and directly transfer the knowledge from LiDAR modality to image
modality on both features and responses. Moreover, we further extend CMKD as a
semi-supervised training framework by distilling knowledge from large-scale
unlabeled data and significantly boost the performance. Until submission, CMKD
ranks $1^{st}$ among the monocular 3D detectors with publications on both KITTI
$test$ set and Waymo $val$ set with significant performance gains compared to
previous state-of-the-art methods.
