Join our upcoming webinar “Deriving Business Value from LLMs and RAGs.”
Register now

We are often oblivious to the capabilities of our minds and senses when it comes to assessing the world around us. Our brains are granted the capability to scan, sort, and navigate the world around us in real-time, which is precisely what computer vision aims to replicate. We're coming closer and closer to developing artificial intelligence that mimics the full spectrum of human capabilities down to the very last detail. Until we get there, let's take a closer look at what panoptic segmentation entails, address common misconceptions related to similar image segmentation techniques, and understand what role panoptic segmentation has in our current world and the near future of AI.

Without further ado, let's take a deeper look at:

  • What is panoptic segmentation?
  • How it works
  • Panoptic segmentation datasets
  • Use cases and applications
  • Key takeaways

What is panoptic segmentation

Panoptic segmentation is a complex computer vision task that solves both instance segmentation and semantic segmentation problems together, enabling a more detailed understanding of a given scene. A closer look at the meaning of the word itself infers that we'll receive a full, comprehensive view of something, which is quite accurate in the case of the image segmentation task(it is relatively simple to grasp if you have prior knowledge of image segmentation). Being a merge of two notable types of image segmentation at its core, panoptic segmentation simultaneously segments and labels an object instance in the required image, providing a comprehensive understanding of the scene. To provide a better definition of panoptic segmentation, we need to not only highlight differences and similarities of panoptic segmentation vs semantic segmentation but how it differs from instance segmentation as well.

Semantic vs. instance segmentation

In short, semantic segmentation refers to segmenting pixels of an image on a granular level to assign a class label to each pixel. We end up with a color-coded output that identifies the limits of the pixels which fall under the same class level. For example, any pixel that belongs to a visualization of a cat will be marked as such. Semantic segmentation covers all pixels of the image and classifies them, as opposed to instance segmentation which concentrates on the ones which have instances (more on that in a moment).

With instance segmentation, objects in a similar class are not lumped together, but they are all highlighted as separate instances. Instance segmentation metrics are typically used to assess the performance and accuracy of algorithms while segmenting particular objects in an image. For example, if we have an image of a road with several cars, each car is labeled as a ‘car' but marked as an individual instance since each car is a different color, brand, etc.

what is panoptic segmentation

How panoptic segmentation works

In order to achieve a comprehensive output, specialists in the field have found that the most optimal path to take is by combining the results of separate semantic segmentation and instance segmentation tasks via a network. Of course, that is easier said than done as the actual breakdown of the architecture is much more complex. Let's take a look at a couple of the most notable panoptic segmentation architectures.

EfficientPS architecture

The common drawback of initial panoptic segmentation models was that the instance segmentation and semantic segmentation network predictions were combined only during the post-processing phase. This led to an array of shortcomings, such as data discrepancies between the two networks and computational overhead. The aim of EfficientPS is to address those challenges via an architecture that consists of a shared backbone, a two-way Feature Pyramid Network (FPN), instance and semantic heads, and a panoptic fusion module. Thanks to the two-way FPN and the separate heads, each consisting of three modules that capture fine features, the losses in data are minimized. Finally, the panoptic fusion module is applied to combine the semantic and instance heads' outputs to produce the panoptic segmentation output. It is also important to highlight evaluation metrics in panoptic segmentation, as it has a significant role in quantifying the accuracy and performance of algorithms in this task.

efficientps architecture
Illustration of the EfficientPS architecture. Image credits: Robot Learning Lab

Attention-guided unified network for panoptic segmentation

In the image above, we can see a visual representation of the next proposed network. Once again, FPN is the backbone that shares features with three parallel branches which are the foreground, background, and RPN branches. And again, the task of panoptic segmentation architecture is to generate an accurate output with minimal to no discrepancies or data loss in terms of ‘things' and ‘stuff' since they are generated from separate models. With the Attention-Guided Unified Network, that challenge is approached by creating a framework where the foreground (FG, also known as instance-level) and background (BG, also known as semantic-level) branches are segmented simultaneously. With the addition of two attention sources — the proposal attention module and the mask attention module, there will be significant accuracy gains in the final output result.

attention guided unified network structure
Attention-Guided Unified Network Structure. Image credits:

Stuff & things in panoptic segmentation

Both semantic segmentation and instance segmentation techniques share the objective of effectively analyzing a scene. It is desirable to accurately detect and identify both "stuff" and "things" within a scene to enable the development of practical applications in the real world. To address this, researchers have devised a solution known as panoptic segmentation, which aims to reconcile the processing of both stuff and things within a scene. The distinction between semantic vs instance vs panoptic segmentation arises from their respective approaches to handling the objects and elements within an image. Now, circling back to panoptic segmentation, it becomes easier to navigate just how it combines the core purposes of both semantic and instance segmentation. Each pixel in an image is assigned a class label but is also identified by the instance that they belong to as well. That means each pixel in the image is assigned two values simultaneously — a label and an instance number. You'll find that the “first” instance of a particular class is marked as ‘0' and then ‘1' for the second instance and so on depending on how many countable instances are visible.

One of the elements that make panoptic segmentation in machine learning stand out from the other image segmentation techniques is its ability to provide an accurate representation of a specific view by including both ‘stuff' and ‘things' in the output.

  • Things: In the realm of computer vision, "things" typically refer to objects that have well-defined geometry and are countable in an image such as people, vehicles, plants, and so on, while
  • Stuff: "Stuff" is the tag employed to describe objects that lack precise geometry but are primarily characterized by their texture and material. They are the elements that are difficult to quantify, such as roads, the sky, and other backgrounds.

By now, we can get a grip on semantic and instance segmentation: Semantic segmentation is essentially a task that concentrates on stuff while instance segmentation focuses on things; panoptic segmentation doesn't exclude one or the other.

Panoptic segmentation datasets

In order to build and deploy a successful system that utilizes the immense advantages panoptic segmentation has to offer, you will need to acquire accurate training data. Thankfully, there are easily accessible public datasets for machine learning that you can implement instead of training your AI system from the ground up. A few of our recommendations are:

COCO — Short for Common Object in Context, the COCO dataset provides image annotations for upwards of 1.5 million common object instances. No need to manually annotate the objects that appear frequently.

coco dataset

Cityscapes — Everything you need to label the urban city life scene is available in this dataset. It includes 10 things categories and 20 stuff categories, including everything from pedestrians to buildings.


Pastis — This is an excellent dataset for applications of AI in agriculture. It comprises 2,433 patches with panoptic annotations for each pixel.

Don't forget to search independently for others depending on your specific preferences and needs since there are dozens of public datasets available online.


Use cases and applications of panoptic segmentation

You can imagine just how much panoptic segmentation has skyrocketed accuracy in computer vision applications by providing a comprehensive and detailed view of images and real-time video. When combined, the previously mentioned image segmentation techniques provide numerous practical applications that contribute to enhancing human capabilities. Here are only a handful of use cases where panoptic segmentation plays an immense role in taking innovation to the next level.

  • Self-driving vehicles — Panoptic segmentation is crucial for establishing safety and efficiency in autonomous vehicles, as it enables the AI system to generate segmentation masks that differentiate between other vehicles, pedestrians, and road signs simultaneously and in real time. This allows the system to accurately assess the surrounding situation and make prompt decisions. To accomplish this, appropriate hardware such as LiDAR cameras and sensors are employed.
  • Medical imaging — Visualizing cell nuclei is a task that requires precision, especially to diagnose diseases like cancer. Often, it's difficult to accurately detect cells during the screening that overlap and are diverse in nature. Semantic segmentation models were commonly used but showcased gaps in data and inaccuracies in the case of overlapping cells. Panoptic segmentation, specifically with deep learning, has proven to outperform the previous technologies.
  • Smart cities — Computer vision and AI play a vital role in constructing smart cities. With the help of state-of-the-art systems, cities can monitor, manage, and optimize all spheres from utilities, to waste management, security, healthcare, education, roads, and much more. Panoptic segmentation offers an accurate and efficient model for smart cities to rely on. Think of the importance panoptic segmentation has for autonomous vehicles and expand it across an entire city.
  • Augmented reality (AR) and virtual reality (VR) — By precisely segmenting and comprehending the objects and scenes, panoptic segmentation assigns accurate boundaries and classifications, thereby enhancing the quality of AR and VR simulations.
  • Surveillance and security: Panoptic segmentation is used in video surveillance systems to identify and track objects of interest within packed scenes, improving security and threat detection.

Key Takeaways

As an image segmentation technique, panoptic segmentation is essentially a marriage of instance segmentation and semantic segmentation. A panoptic segmentation task provides us with a clear and detailed output regarding the entire scene of an image or real-time video. The final output does not polarize merely ‘things' or ‘stuff'. Instead, each pixel in the image is assigned a label and a corresponding instance ID to provide us with the full picture of the input visual. It enables us to excel in terms of accuracy when producing both ML or DL-based algorithms for computer vision applications. Can a new task emerge one day that develops and innovates image segmentation further? Perhaps, yes, but panoptic segmentation is undoubtedly revolutionary for our current computer vision tasks and opens endless avenues for innovation.

Recommended for you

Stay connected

Subscribe to receive new blog posts and latest discoveries in the industry from SuperAnnotate