  Monocular 3D object detection is an important task for autonomous driving
considering its advantage of low cost. It is much more challenging compared to
conventional 2D case due to its inherent ill-posed property, which is mainly
reflected on the lack of depth information. Recent progress on 2D detection
offers opportunities to better solving this problem. However, it is non-trivial
to make a general adapted 2D detector work in this 3D task. In this technical
report, we study this problem with a practice built on fully convolutional
single-stage detector and propose a general framework FCOS3D. Specifically, we
first transform the commonly defined 7-DoF 3D targets to image domain and
decouple it as 2D and 3D attributes. Then the objects are distributed to
different feature levels with the consideration of their 2D scales and assigned
only according to the projected 3D-center for training procedure. Furthermore,
the center-ness is redefined with a 2D Guassian distribution based on the
3D-center to fit the 3D target formulation. All of these make this framework
simple yet effective, getting rid of any 2D detection or 2D-3D correspondence
priors. Our solution achieves 1st place out of all the vision-only methods in
the nuScenes 3D detection challenge of NeurIPS 2020. Code and models are
released at https://github.com/open-mmlab/mmdetection3d.
