# Learning Panoptic Segmentation from Instance Contours

arXiv: https://arxiv.org/abs/2010.11681 · HTML: https://mlsanity.com/paper/2010.11681/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Panoptic Segmentation: A Review](https://mlsanity.com/paper/2111.10250/) (2021)
- [FisheyeDetNet: 360deg Surround view Fisheye Camera based Object Detection System for Autonomous Driving](https://mlsanity.com/paper/2404.13443/) (2024)
- [Pseudo-Boolean Polynomials Approach To Edge Detection And Image Segmentation](https://mlsanity.com/paper/2308.15453/) (2023)
- [Cross-Domain Semantic Segmentation with Large Language Model-Assisted Descriptor Generation](https://mlsanity.com/paper/2501.16467/) (2025)

## Abstract (arXiv)

Panoptic Segmentation aims to provide an understanding of background (stuff) and instances of objects (things) at a pixel level. It combines the separate tasks of semantic segmentation (pixel level classification) and instance segmentation to build a single unified scene understanding task. Typically, panoptic segmentation is derived by combining semantic and instance segmentation tasks that are learned separately or jointly (multi-task networks). In general, instance segmentation networks are built by adding a foreground mask estimation layer on top of object detectors or using instance clustering methods that assign a pixel to an instance center. In this work, we present a fully convolution neural network that learns instance segmentation from semantic segmentation and instance contours (boundaries of things). Instance contours along with semantic segmentation yield a boundary aware semantic segmentation of things. Connected component labeling on these results produces instance segmentation. We merge semantic and instance segmentation results to output panoptic segmentation. We evaluate our proposed method on the CityScapes dataset to demonstrate qualitative and quantitative performances along with several ablation studies. Our overview video can be accessed from url:https://youtu.be/wBtcxRhG3e0.