  3D object detection in autonomous driving aims to reason "what" and "where"
the objects of interest present in a 3D world. Following the conventional
wisdom of previous 2D object detection, existing methods often adopt the
canonical Cartesian coordinate system with perpendicular axis. However, we
conjugate that this does not fit the nature of the ego car's perspective, as
each onboard camera perceives the world in shape of wedge intrinsic to the
imaging geometry with radical (non-perpendicular) axis. Hence, in this paper we
advocate the exploitation of the Polar coordinate system and propose a new
Polar Transformer (PolarFormer) for more accurate 3D object detection in the
bird's-eye-view (BEV) taking as input only multi-camera 2D images.
Specifically, we design a cross attention based Polar detection head without
restriction to the shape of input structure to deal with irregular Polar grids.
For tackling the unconstrained object scale variations along Polar's distance
dimension, we further introduce a multi-scalePolar representation learning
strategy. As a result, our model can make best use of the Polar representation
rasterized via attending to the corresponding image observation in a
sequence-to-sequence fashion subject to the geometric constraints. Thorough
experiments on the nuScenes dataset demonstrate that our PolarFormer
outperforms significantly state-of-the-art 3D object detection alternatives.
