What is the main difference between panoptic segmentation and instance segmentation?

The main difference between panoptic segmentation and instance segmentation lies in how they handle the entire image and the types of objects they segment. Instance segmentation focuses on segmenting individual instances of countable objects, often called 'things,' such as cars, people, or animals. It identifies each object separately, even if they belong to the same class. However, instance segmentation typically ignores background regions or 'stuff' like grass, sky, or roads. Panoptic segmentation, on the other hand, aims to provide a complete scene understanding by segmenting all pixels in an image, classifying them into either 'things' or 'stuff'. It segments individual instances of 'things' and also provides semantic labels for 'stuff' regions. This means that panoptic segmentation combines the tasks of instance segmentation and semantic segmentation into a single unified framework. For example, in an image of a street scene, instance segmentation would identify and segment individual cars and pedestrians, but it would ignore the road, sky, and buildings. Panoptic segmentation would not only identify and segment individual cars and pedestrians but also label the road, sky, and buildings as 'stuff' regions, providing a complete segmentation of the entire scene. Therefore, panoptic segmentation provides a more comprehensive scene understanding than instance segmentation by segmenting all pixels in the image, including both 'things' and 'stuff'.