  Recently, polar-based representation has shown promising properties in
perceptual tasks. In addition to Cartesian-based approaches, which separate
point clouds unevenly, representing point clouds as polar grids has been
recognized as an alternative due to (1) its advantage in robust performance
under different resolutions and (2) its superiority in streaming-based
approaches. However, state-of-the-art polar-based detection methods inevitably
suffer from the feature distortion problem because of the non-uniform division
of polar representation, resulting in a non-negligible performance gap compared
to Cartesian-based approaches. To tackle this issue, we present PARTNER, a
novel 3D object detector in the polar coordinate. PARTNER alleviates the
dilemma of feature distortion with global representation re-alignment and
facilitates the regression by introducing instance-level geometric information
into the detection head. Extensive experiments show overwhelming advantages in
streaming-based detection and different resolutions. Furthermore, our method
outperforms the previous polar-based works with remarkable margins of 3.68% and
9.15% on Waymo and ONCE validation set, thus achieving competitive results over
the state-of-the-art methods.
