Monocular 3D object detection (M3OD) is a significant yet inherently
challenging task in autonomous driving due to absence of implicit 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.

通过半监督学习，利用大量未标记的数据，我们提出了 ODM3D 框架，通过跨模态知识蒸馏和新增的数据增强策略来提高单眼 3D 物体检测器的性能，取得了在 KITTI 验证和测试基准上的第一名成绩，显著超过所有现有的单眼方法（监督或半监督）在 BEV 和 3D 检测指标上。