  Depth-aware video panoptic segmentation is a promising approach to camera
based scene understanding. However, the current state-of-the-art methods
require costly video annotations and use a complex training pipeline compared
to their image-based equivalents. In this paper, we present a new approach
titled Unified Perception that achieves state-of-the-art performance without
requiring video-based training. Our method employs a simple two-stage cascaded
tracking algorithm that (re)uses object embeddings computed in an image-based
network. Experimental results on the Cityscapes-DVPS dataset demonstrate that
our method achieves an overall DVPQ of 57.1, surpassing state-of-the-art
methods. Furthermore, we show that our tracking strategies are effective for
long-term object association on KITTI-STEP, achieving an STQ of 59.1 which
exceeded the performance of state-of-the-art methods that employ the same
backbone network.
  Code is available at: https://tue-mps.github.io/unipercept
