What AR computer vision techniques improve accuracy?

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Augmented reality (AR) is the technology that overlays digital information on the real world, creating immersive and interactive experiences. AR applications rely on computer vision, the field of computer science that enables machines to understand and manipulate visual data. However, computer vision is not a trivial task, especially in dynamic and complex environments. How can AR developers improve the accuracy of their computer vision techniques and deliver more realistic and robust AR experiences? Here are some of the best practices to consider.

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Avish Saini

Software Engineer || XR Enthusiast || 7+ Hackathons victories?? || Ex Core Member at DSC KIET || Ex Innogeeks

1 Use multiple sensors

One of the challenges of computer vision is to deal with the variability of lighting, occlusion, and perspective in the real world. To overcome this, AR developers can use multiple sensors, such as cameras, depth sensors, inertial measurement units (IMUs), and global positioning systems (GPS), to capture different aspects of the scene and fuse them together. For example, depth sensors can provide information about the distance and shape of objects, while IMUs can measure the orientation and motion of the device. By combining these data sources, AR applications can achieve more accurate and stable tracking, registration, and occlusion handling.

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Avish Saini

Software Engineer || XR Enthusiast || 7+ Hackathons victories?? || Ex Core Member at DSC KIET || Ex Innogeeks
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One of the technique that improves accuracy in AR computer vision is feature-based tracking. This method involves identifying and tracking distinct features or keypoints in the environment, such as corners, edges, or unique patterns. By robustly matching these features across frames, AR systems can accurately estimate the camera pose relative to the scene, allowing for more precise registration of virtual content. Additionally, feature-based tracking techniques often incorporate methods like optical flow or geometric constraints to further enhance accuracy in challenging scenarios, such as low-light conditions or rapid camera motion.

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Akshay Kumar

Solution Architect @ VELUX | XR Solution Built & Design, Unity, C#, Unreal, C++, Python
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In AR, techniques like environmental lighting estimation, feature point detection, plane detection, and occlusion handling enhance accuracy. Environmental lighting adjusts virtual objects to match real-world lighting. Feature point detection ensures precise alignment. Plane detection identifies flat surfaces, and occlusion handling ensures realistic object interactions. Integrating these techniques improves AR accuracy and realism.

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2 Apply machine learning

Another way to improve the accuracy of computer vision is to apply machine learning, the branch of artificial intelligence that enables machines to learn from data and perform tasks without explicit programming. Machine learning can help AR applications to recognize, classify, and segment objects, faces, and scenes in the real world, as well as to generate realistic and adaptive content. For example, machine learning can enable AR applications to detect and track objects of interest, such as landmarks, logos, or products, and overlay relevant information or animations on them. Machine learning can also enable AR applications to create realistic 3D models of objects or environments from images or videos, and to adjust the appearance and behavior of the digital content according to the context and user feedback.

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Akshay Kumar

Solution Architect @ VELUX | XR Solution Built & Design, Unity, C#, Unreal, C++, Python
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Applying machine learning, like Google's Geospatial Creator, enables AR to place virtual objects accurately in the real world. For example, it can recognize surfaces and align virtual objects seamlessly, allowing users to place virtual furniture in their living room or view historical landmarks in their actual locations, enhancing their AR experience with realism and interactivity.

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3 Optimize performance

The performance of the AR device and application is a key factor that affects the accuracy of computer vision. To ensure efficient processing and rendering of visual data in real time, AR developers can use various techniques to optimize the performance of computer vision. These include reducing the resolution and frame rate of the camera input, using efficient algorithms and libraries for image processing and computer vision, leveraging cloud computing and edge computing for offloading computation and storage, implementing caching and prefetching strategies for data transmission, balancing the quality and quantity of digital content, and testing and benchmarking the application on different devices and scenarios. By following these best practices, AR developers can improve the accuracy of their computer vision techniques while delivering engaging and reliable AR experiences to their users.

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4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What AR computer vision techniques improve accuracy?

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1 Use multiple sensors

2 Apply machine learning

3 Optimize performance

4 Here’s what else to consider

Augmented Reality

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What AR computer vision techniques improve accuracy?

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1 Use multiple sensors

2 Apply machine learning

3 Optimize performance

4 Here’s what else to consider

Augmented Reality

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