  3D object detection is an important capability needed in various practical
applications such as driver assistance systems. Monocular 3D detection, as a
representative general setting among image-based approaches, provides a more
economical solution than conventional settings relying on LiDARs but still
yields unsatisfactory results. This paper first presents a systematic study on
this problem. We observe that the current monocular 3D detection can be
simplified as an instance depth estimation problem: The inaccurate instance
depth blocks all the other 3D attribute predictions from improving the overall
detection performance. Moreover, recent methods directly estimate the depth
based on isolated instances or pixels while ignoring the geometric relations
across different objects. To this end, we construct geometric relation graphs
across predicted objects and use the graph to facilitate depth estimation. As
the preliminary depth estimation of each instance is usually inaccurate in this
ill-posed setting, we incorporate a probabilistic representation to capture the
uncertainty. It provides an important indicator to identify confident
predictions and further guide the depth propagation. Despite the simplicity of
the basic idea, our method, PGD, obtains significant improvements on KITTI and
nuScenes benchmarks, achieving 1st place out of all monocular vision-only
methods while still maintaining real-time efficiency. Code and models will be
released at https://github.com/open-mmlab/mmdetection3d.
