Machine learning in AR?
When you talk about Augmented Reality, the first thing that usually comes to mind is snap chat filters and games like Pokémon Go.
But the world of AR has expensed far more than the trivial implementation we are all used to.
This has been further enhanced with the introduction of machine learning technology. To create a combination that is just as eventful, powerful, and potentially game-changing – both in the development and enterprise circles, plus the consumer experience market.
The matchup between the two pairs has resulted in some very interesting changes.
This means AR now has not just real-world applications but a monetary value. Its use can expand or gain businesses the advantage, control, management or maintenance it needs.
At its core, AR is driven by advanced computer vision algorithms. But we can do more. By layering machine learning systems on top of the core AR tech, the range of possible use cases can be expanded greatly. In the following sections, I will show you how.
Computer vision enhanced with AR
One key area that augmented reality is really taking a strong foothold is in the field of computer vision. With the use of simultaneous localization and mapping, we get a computer vision algorithm that compares visual features between camera frames in order to map and track the environment.
When combined with sensor data from available devices such as smartphone gyroscope and accelerometer, it is possible to the precision of the tracking data.
There is a strong market drive to get the technology developed as soon as possible. That is why companies such as Apple, Google and others are restlessly working to improve these algorithms. This leaves developers with the task to continuously build better and more reliable applications using AR.
Computer vision at the edge
The frameworks at the core of the AR technology stack often provide nest extra features like image tracking, pose estimation and, recently with ARKit 3.0, people occlusion. Many of these features are powered by machine learning.
If we take all the above and convert it into real-world benefits we start to see a wider possibility that the technology can unlock.
Consumer Based: Visual Search and Visual Exploration
We all know that search is primarily dominated by text, but what do you do if your search item is primarily visual? This is where Machine learning comes into play, by giving companies the ability to implement visual searches.
A simple example of this is a search that allows customers to upload pictures to a website and “search” similar items. Then the ML algorithms take over, it lets the application “see” what is being searched and matches it to available inventory.
Once customers find the item they want or need, they can also use AR to “try on” those items. If it’s a piece of clothing, they can get a feel for it on their body type. If it’s a piece of furniture or accessory, they can see it in their space directly.
The entire process helps customers shop online more efficiently and more confidently.
BMW, Pinterest and Ikea, to name a few, are already implementing these types of AR programs, and soon it might be more common to see these programs than not in online commerce.
Business to Business Solutions: Estimates and Exploration
Contrary to popular belief, Augmented Reality goes beyond consumer-facing applications.
In the B2B world, proposals and quotes have a high element of visualization. With the implementation of ML into the world of AR, we can finally take the data available to us and experience it, not just get a report.
For example, if you work in design or architectural firm, you can use ML to examine data from things like topography, existing structures, new building codes, and architectural best practices to help developers visualize your proposal from the ground up.
This type of large visualization gives companies the ability to bring data to life without exporting containers or other tech integrations. AR could be opened on something as basic as an iPad or smartphone and experienced wherever the target party is.
Some of the potential features could include:
- digitized, explorable floor plans
- 3D representations of new structures
- analytics on interactions with the system for further refining
Internal Solutions: Training and Certifications
Experiential training is expensive and sometimes dangerous.
The standard model of learning has always been with the use of textbooks or written materials, which may offer knowledge, but this gives you a fixed point of view of the information it contains. Yet to fully understand and gain knowledge, you can not rely solely on what is read without practical application.
This is why businesses are looking into being able to use the power of ML as the foundation for AR-based, experiential training.
Machine Learning could create personalized educational experiences designed to take advantage of an employee’s current knowledge, knowledge gaps, and best learning practices.
Then, it can provide ongoing training that integrates well with the job itself.
The AR layer can provide experiences that mimic real-life conditions without harming either the student or the recipient — think surgeries or military operations. Computer intelligence processes the data from interactions for continual refinement of the educational experience.
What Machine Learning Brings to the Table
In all these situations, the purpose of ML is to bring context into the world of AR. We take real-world items and scenarios, add context with AR with customized experiences that are a result of Machine Learning.
At the end of the day, the real advantage of ML in the world of business is using context to answer customer inquiries, quell fears and concerns, and field different scenarios. In a nutshell, ML makes use of a large subset of data in conjunction with AR to bring meaning to what users perceive and view.
This is further made possible since consumers and B2B clients don’t have to invest in any tech to take advantage of these technologies. Most of your customers already can participate in this type of tech.
The next step forward as users is to the adoption of ML designed to optimize the experience. And in return, you get valuable data, processed and ready to go.
AR and machine learning: what comes next?
We have always stated that the possibility of Augmented Reality is far beyond what the initial developers envisioned. It has more or less taken a life of its own. Now when paired with the power of Machine Learning, we are seeing just how far we can push the envelope.
But this is only the beginning. There is still a lot of work yet to be done within this field. And the inherent business advantage is what is driving the advancements.