  Monocular 3D object detection is an important task for autonomous driving
considering its advantage of low cost. It is much more challenging than
conventional 2D cases due to its inherent ill-posed property, which is mainly
reflected in 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 paper, we
study this problem with a practice built on a fully convolutional single-stage
detector and propose a general framework FCOS3D. Specifically, we first
transform the commonly defined 7-DoF 3D targets to the image domain and
decouple them as 2D and 3D attributes. Then the objects are distributed to
different feature levels with consideration of their 2D scales and assigned
only according to the projected 3D-center for the training procedure.
Furthermore, the center-ness is redefined with a 2D Gaussian 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.
