  Monocular 3D object detection (M3OD) is a significant yet inherently
challenging task in autonomous driving due to absence of explicit depth cues in
a single RGB image. In this paper, we strive to boost currently underperforming
monocular 3D object detectors by leveraging an abundance of unlabelled data via
semi-supervised learning. Our proposed ODM3D framework entails cross-modal
knowledge distillation at various levels to inject LiDAR-domain knowledge into
a monocular detector during training. By identifying foreground sparsity as the
main culprit behind existing methods' suboptimal training, we exploit the
precise localisation information embedded in LiDAR points to enable more
foreground-attentive and efficient distillation via the proposed BEV occupancy
guidance mask, leading to notably improved knowledge transfer and M3OD
performance. Besides, motivated by insights into why existing cross-modal
GT-sampling techniques fail on our task at hand, we further design a novel
cross-modal object-wise data augmentation strategy for effective RGB-LiDAR
joint learning. Our method ranks 1st in both KITTI validation and test
benchmarks, significantly surpassing all existing monocular methods, supervised
or semi-supervised, on both BEV and 3D detection metrics.
