  We propose a novel semi-supervised active learning (SSAL) framework for
monocular 3D object detection with LiDAR guidance (MonoLiG), which leverages
all modalities of collected data during model development. We utilize LiDAR to
guide the data selection and training of monocular 3D detectors without
introducing any overhead in the inference phase. During training, we leverage
the LiDAR teacher, monocular student cross-modal framework from semi-supervised
learning to distill information from unlabeled data as pseudo-labels. To handle
the differences in sensor characteristics, we propose a data noise-based
weighting mechanism to reduce the effect of propagating noise from LiDAR
modality to monocular. For selecting which samples to label to improve the
model performance, we propose a sensor consistency-based selection score that
is also coherent with the training objective. Extensive experimental results on
KITTI and Waymo datasets verify the effectiveness of our proposed framework. In
particular, our selection strategy consistently outperforms state-of-the-art
active learning baselines, yielding up to 17% better saving rate in labeling
costs. Our training strategy attains the top place in KITTI 3D and
birds-eye-view (BEV) monocular object detection official benchmarks by
improving the BEV Average Precision (AP) by 2.02.
