# Fast Panoptic Segmentation Network

arXiv: https://arxiv.org/abs/1910.03892 · HTML: https://mlsanity.com/paper/1910.03892/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 19 of 19)

- [Part-aware Panoptic Segmentation](https://mlsanity.com/paper/2106.06351/) (2021)
- [You Only Segment Once: Towards Real-Time Panoptic Segmentation](https://mlsanity.com/paper/2303.14651/) (2023)
- [PEM: Prototype-based Efficient MaskFormer for Image Segmentation](https://mlsanity.com/paper/2402.19422/) (2024)
- [Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments](https://mlsanity.com/paper/2207.04526/) (2022)
- [MGNet: Monocular Geometric Scene Understanding for Autonomous Driving](https://mlsanity.com/paper/2206.13199/) (2022)
- [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)
- [Hierarchical Lov'asz Embeddings for Proposal-free Panoptic Segmentation](https://mlsanity.com/paper/2106.04555/) (2021)
- [Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics](https://mlsanity.com/paper/2303.04302/) (2023)
- [Intra-Batch Supervision for Panoptic Segmentation on High-Resolution Images](https://mlsanity.com/paper/2304.08222/) (2023)
- [Time-Space Transformers for Video Panoptic Segmentation](https://mlsanity.com/paper/2210.03546/) (2022)
- [PanDepth: Joint Panoptic Segmentation and Depth Completion](https://mlsanity.com/paper/2212.14180/) (2022)
- [The revenge of BiSeNet: Efficient Multi-Task Image Segmentation](https://mlsanity.com/paper/2404.09570/) (2024)
- [IrrNet: Advancing Irrigation Mapping with Incremental Patch Size Training on Remote Sensing Imagery](https://mlsanity.com/paper/2404.11762/) (2024)
- [Progressive Token Length Scaling in Transformer Encoders for Efficient Universal Segmentation](https://mlsanity.com/paper/2404.14657/) (2024)
- [Panoptic Segmentation of Environmental UAV Images : Litter Beach](https://mlsanity.com/paper/2508.15985/) (2025)
- [SUNet: Scale-aware Unified Network for Panoptic Segmentation](https://mlsanity.com/paper/2209.02877/) (2022)
- [PENet: A Joint Panoptic Edge Detection Network](https://mlsanity.com/paper/2303.08848/) (2023)
- [IrrMap: A Large-Scale Comprehensive Dataset for Irrigation Method Mapping](https://mlsanity.com/paper/2505.08273/) (2025)

## Abstract (arXiv)

In this work, we present an end-to-end network for fast panoptic segmentation. This network, called Fast Panoptic Segmentation Network (FPSNet), does not require computationally costly instance mask predictions or merging heuristics. This is achieved by casting the panoptic task into a custom dense pixel-wise classification task, which assigns a class label or an instance id to each pixel. We evaluate FPSNet on the Cityscapes and Pascal VOC datasets, and find that FPSNet is faster than existing panoptic segmentation methods, while achieving better or similar panoptic segmentation performance. On the Cityscapes validation set, we achieve a Panoptic Quality score of 55.1%, at prediction times of 114 milliseconds for images with a resolution of 1024x2048 pixels. For lower resolutions of the Cityscapes dataset and for the Pascal VOC dataset, FPSNet runs at 22 and 35 frames per second, respectively.