Panoptic Segmentation

We propose and study a task we name panoptic segmentation (PS). Panoptic segmentation unifies the typically distinct tasks of semantic segmentation (assign a class label to each pixel) and instance segmentation (detect and segment each object instance). The proposed task requires generating a coherent scene segmentation that is rich and complete, an important step toward real-world vision systems. While early work in computer vision addressed related image/scene parsing tasks, these are not currently popular, possibly due to lack of appropriate metrics or associated recognition challenges. To address this, we propose a novel panoptic quality (PQ) metric that captures performance for all classes (stuff and things) in an interpretable and unified manner. Using the proposed metric, we perform a rigorous study of both human and machine performance for PS on three existing datasets, revealing interesting insights about the task. The aim of our work is to revive the interest of the community in a more unified view of image segmentation. For more analysis and up-to-date results, please check the arXiv version of the paper: https://arxiv.org/abs/1801.00868.

1. Introduction

In the early days of computer vision, things - countable objects such as people, animals, tools - received the dominant share of attention. Questioning the wisdom of this trend, Adelson [1] elevated the importance of studying systems that recognize stuff - amorphous regions of similar texture or material such as grass, sky, road. This dichotomy between stuff and things persists to this day, reflected in both the division of visual recognition tasks and in the specialized algorithms developed for stuff and thing tasks.