  Bird's-Eye-View (BEV) maps have emerged as one of the most powerful
representations for scene understanding due to their ability to provide rich
spatial context while being easy to interpret and process. Such maps have found
use in many real-world tasks that extensively rely on accurate scene
segmentation as well as object instance identification in the BEV space for
their operation. However, existing segmentation algorithms only predict the
semantics in the BEV space, which limits their use in applications where the
notion of object instances is also critical. In this work, we present the first
BEV panoptic segmentation approach for directly predicting dense panoptic
segmentation maps in the BEV, given a single monocular image in the frontal
view (FV). Our architecture follows the top-down paradigm and incorporates a
novel dense transformer module consisting of two distinct transformers that
learn to independently map vertical and flat regions in the input image from
the FV to the BEV. Additionally, we derive a mathematical formulation for the
sensitivity of the FV-BEV transformation which allows us to intelligently
weight pixels in the BEV space to account for the varying descriptiveness
across the FV image. Extensive evaluations on the KITTI-360 and nuScenes
datasets demonstrate that our approach exceeds the state-of-the-art in the PQ
metric by 3.61 pp and 4.93 pp respectively.
