Monocular 3D detection currently struggles with extremely lower detection rates compared to LiDAR-based methods. The poor accuracy is mainly caused by the absence of accurate location cues due to the ill-posed nature of monocular imagery. LiDAR point clouds, which provide precise spatial measurement, can offer beneficial information for the training of monocular methods. To make use of LiDAR point clouds, prior works project them to form depth map labels, subsequently training a dense depth estimator to extract explicit location features. This indirect and complicated way introduces intermediate products, i.e., depth map predictions, taking much computation costs as well as leading to suboptimal performances. In this paper, we propose LPCG (LiDAR point cloud guided monocular 3D object detection), which is a general framework for guiding the training of monocular 3D detectors with LiDAR point clouds. Specifically, we use LiDAR point clouds to generate pseudo labels, allowing monocular 3D detectors to benefit from easy-collected massive unlabeled data. LPCG works well under both supervised and unsupervised setups. Thanks to a general design, LPCG can be plugged into any monocular 3D detector, significantly boosting the performance. As a result, we take the first place on KITTI monocular 3D/BEV (bird's-eye-view) detection benchmark with a considerable margin. The code will be made publicly available soon.
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