# PanoNet: Real-time Panoptic Segmentation through Position-Sensitive Feature Embedding

arXiv: https://arxiv.org/abs/2008.00192 · HTML: https://mlsanity.com/paper/2008.00192/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [You Only Segment Once: Towards Real-Time Panoptic Segmentation](https://mlsanity.com/paper/2303.14651/) (2023)
- [Panoptic Segmentation: A Review](https://mlsanity.com/paper/2111.10250/) (2021)
- [Embedded Vision for Self-Driving on Forest Roads](https://mlsanity.com/paper/2105.13754/) (2021)
- [CFNet: Learning Correlation Functions for One-Stage Panoptic Segmentation](https://mlsanity.com/paper/2201.04796/) (2022)
- [On depth prediction for autonomous driving using self-supervised learning](https://mlsanity.com/paper/2403.06194/) (2024)

## Abstract (arXiv)

We propose a simple, fast, and flexible framework to generate simultaneously semantic and instance masks for panoptic segmentation. Our method, called PanoNet, incorporates a clean and natural structure design that tackles the problem purely as a segmentation task without the time-consuming detection process. We also introduce position-sensitive embedding for instance grouping by accounting for both object's appearance and its spatial location. Overall, PanoNet yields high panoptic quality results of high-resolution Cityscapes images in real-time, significantly faster than all other methods with comparable performance. Our approach well satisfies the practical speed and memory requirement for many applications like autonomous driving and augmented reality.