  Dense panoptic prediction is a key ingredient in many existing applications
such as autonomous driving, automated warehouses or remote sensing. Many of
these applications require fast inference over large input resolutions on
affordable or even embedded hardware. We propose to achieve this goal by
trading off backbone capacity for multi-scale feature extraction. In comparison
with contemporaneous approaches to panoptic segmentation, the main novelties of
our method are efficient scale-equivariant feature extraction, cross-scale
upsampling through pyramidal fusion and boundary-aware learning of
pixel-to-instance assignment. The proposed method is very well suited for
remote sensing imagery due to the huge number of pixels in typical city-wide
and region-wide datasets. We present panoptic experiments on Cityscapes,
Vistas, COCO and the BSB-Aerial dataset. Our models outperform the state of the
art on the BSB-Aerial dataset while being able to process more than a hundred
1MPx images per second on a RTX3090 GPU with FP16 precision and TensorRT
optimization.
