Semantic segmentation assigns a class label to every pixel in an image, treating all pixels of a class as a unified segment without distinguishing between different instances of the same class. For example, all pixels belonging to "road" are labeled as such, without differentiating between different stretches of road. This method is useful for applications where understanding the general scene layout is more important than identifying individual objects.
Instance segmentation goes a step further by distinguishing between individual instances of the same object class. Each object is separately labeled and segmented, making it possible to differentiate between multiple instances, such as several cars in a parking lot. This is crucial for tasks requiring detailed analysis of scenes with multiple objects, like in autonomous driving, where distinguishing between multiple pedestrians or vehicles is necessary for navigation and safety.
Panoptic segmentation combines the strengths of both semantic and instance segmentation by labeling each pixel with both a class and an instance ID. This approach ensures that both general classes (like "sky" or "road") and individual objects (like specific cars or people) are accurately identified and differentiated. Panoptic segmentation provides a holistic understanding of the scene, integrating both object and background information, which is valuable for applications like urban planning and autonomous systems.