# Single-Shot Panoptic Segmentation

arXiv: https://arxiv.org/abs/1911.00764 · HTML: https://mlsanity.com/paper/1911.00764/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2019.

## Built on by (most cited first, 9 of 9)

- [STEP: Segmenting and Tracking Every Pixel](https://mlsanity.com/paper/2102.11859/) (2021)
- [Waymo Open Dataset: Panoramic Video Panoptic Segmentation](https://mlsanity.com/paper/2206.07704/) (2022)
- [DeepLab2: A TensorFlow Library for Deep Labeling](https://mlsanity.com/paper/2106.09748/) (2021)
- [Panoptic Segmentation: A Review](https://mlsanity.com/paper/2111.10250/) (2021)
- [Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches](https://mlsanity.com/paper/2206.08016/) (2022)
- [Uncertainty Aware Proposal Segmentation for Unknown Object Detection](https://mlsanity.com/paper/2111.12866/) (2021)
- [SemSegDepth: A Combined Model for Semantic Segmentation and Depth Completion](https://mlsanity.com/paper/2209.00381/) (2022)
- [Time-Space Transformers for Video Panoptic Segmentation](https://mlsanity.com/paper/2210.03546/) (2022)
- [SPVC: Structured and Panoptic Video Fixing for Cross-Dataset Driving Scene Rendering](https://mlsanity.com/paper/2608.17420/) (2026)

## Abstract (arXiv)

We present a novel end-to-end single-shot method that segments countable object instances (things) as well as background regions (stuff) into a non-overlapping panoptic segmentation at almost video frame rate. Current state-of-the-art methods are far from reaching video frame rate and mostly rely on merging instance segmentation with semantic background segmentation, making them impractical to use in many applications such as robotics. Our approach relaxes this requirement by using an object detector but is still able to resolve inter- and intra-class overlaps to achieve a non-overlapping segmentation. On top of a shared encoder-decoder backbone, we utilize multiple branches for semantic segmentation, object detection, and instance center prediction. Finally, our panoptic head combines all outputs into a panoptic segmentation and can even handle conflicting predictions between branches as well as certain false predictions. Our network achieves 32.6% PQ on MS-COCO at 23.5 FPS, opening up panoptic segmentation to a broader field of applications.