  Compared to typical multi-sensor systems, monocular 3D object detection has
attracted much attention due to its simple configuration. However, there is
still a significant gap between LiDAR-based and monocular-based methods. In
this paper, we find that the ill-posed nature of monocular imagery can lead to
depth ambiguity. Specifically, objects with different depths can appear with
the same bounding boxes and similar visual features in the 2D image.
Unfortunately, the network cannot accurately distinguish different depths from
such non-discriminative visual features, resulting in unstable depth training.
To facilitate depth learning, we propose a simple yet effective plug-and-play
module, \underline{O}ne \underline{B}ounding Box \underline{M}ultiple
\underline{O}bjects (OBMO). Concretely, we add a set of suitable pseudo labels
by shifting the 3D bounding box along the viewing frustum. To constrain the
pseudo-3D labels to be reasonable, we carefully design two label scoring
strategies to represent their quality. In contrast to the original hard depth
labels, such soft pseudo labels with quality scores allow the network to learn
a reasonable depth range, boosting training stability and thus improving final
performance. Extensive experiments on KITTI and Waymo benchmarks show that our
method significantly improves state-of-the-art monocular 3D detectors by a
significant margin (The improvements under the moderate setting on KITTI
validation set are $\mathbf{1.82\sim 10.91\%}$ \textbf{mAP in BEV} and
$\mathbf{1.18\sim 9.36\%}$ \textbf{mAP in 3D}). Codes have been released at
\url{https://github.com/mrsempress/OBMO}.
