# Real-Time Panoptic Segmentation from Dense Detections

Source: [Real-Time Panoptic Segmentation from Dense Detections](<https://www.sota2.com/research/paper/68fa67581e209131cea15b1b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:01:46.445Z

Authors: Rui Hou, Jie Li, Arjun Bhargava, Allan Raventos, Vitor Guizilini, Chao Fang, Jerome Lynch, Adrien Gaidon

Published: 2019-12-03T05:50:02.000Z

## Abstract

Panoptic segmentation is a complex full scene parsing task requiring simultaneous instance and semantic segmentation at high resolution. Current state-of-the-art approaches cannot run in real-time, and simplifying these architectures to improve efficiency severely degrades their accuracy. In this paper, we propose a new single-shot panoptic segmentation network that leverages dense detections and a global self-attention mechanism to operate in real-time with performance approaching the state of the art. We introduce a novel parameter-free mask construction method that substantially reduces computational complexity by efficiently reusing information from the object detection and semantic segmentation sub-tasks. The resulting network has a simple data flow that does not require feature map re-sampling or clustering post-processing, enabling significant hardware acceleration. Our experiments on the Cityscapes and COCO benchmarks show that our network works at 30 FPS on 1024x2048 resolution, trading a 3% relative performance degradation from the current state of the art for up to 440% faster inference.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Panoptic Segmentation on COCO (val)](<https://www.sota2.com/research/sota/panoptic-segmentation-on-coco-val.md>): Panoptic Segmentation; COCO (val); PQ: 37.1.

- [Panoptic Segmentation on Cityscapes (val)](<https://www.sota2.com/research/sota/panoptic-segmentation-on-cityscapes-val.md>): Panoptic Segmentation; Cityscapes (val); PQ: 58.8.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1912.01202>)

[PDF](<https://arxiv.org/pdf/1912.01202.pdf>)