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Google ARCore VS OpenCV

Compare Google ARCore VS OpenCV and see what are their differences

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Google ARCore logo Google ARCore

Google Augmented Reality SDK

OpenCV logo OpenCV

OpenCV is the world's biggest computer vision library
  • Google ARCore Landing page
    Landing page //
    2023-07-07
  • OpenCV Landing page
    Landing page //
    2023-07-29

Google ARCore features and specs

  • Wide Device Compatibility
    ARCore supports a wide range of Android devices, making it accessible to a large user base.
  • High-Quality Tracking
    ARCore provides high-precision tracking and environmental understanding, enhancing the user experience in AR applications.
  • Cross-Platform
    ARCore supports not only Android but also iOS through the ARCore SDK for iOS, allowing developers to reach users on both platforms.
  • Integration with Google Services
    ARCore can be easily integrated with other Google services like Maps, enabling developers to create location-based AR experiences.
  • User-Friendly SDK
    Offers a comprehensive SDK with extensive documentation, tutorials, and community support, which eases the development process for beginners and experts alike.

Possible disadvantages of Google ARCore

  • Performance Variability
    Performance and capabilities can vary significantly across different Android devices, potentially leading to inconsistent user experiences.
  • Limited Advanced Features
    While ARCore provides the basic functionalities required for AR, it may lack some of the advanced features available in more specialized AR platforms.
  • Battery Consumption
    ARCore applications can be resource-intensive, leading to higher battery consumption on mobile devices.
  • iOS Limitations
    Although ARCore supports iOS, it may not be as deeply integrated or optimized as native ARKit applications, potentially limiting performance.
  • Dependency on Google Play Services
    ARCore relies on Google Play Services for AR, which may not be available in certain regions or on devices without Google Play support.

OpenCV features and specs

  • Comprehensive Library
    OpenCV offers a wide range of tools for various aspects of computer vision, including image processing, machine learning, and video analysis.
  • Cross-Platform Compatibility
    OpenCV is designed to run on multiple platforms, including Windows, Linux, macOS, Android, and iOS, which makes it versatile for development across different environments.
  • Open Source
    Being open-source, OpenCV is freely available for use and allows developers to inspect, modify, and enhance the code according to their needs.
  • Large Community Support
    A large community of developers and researchers actively contributes to OpenCV, providing extensive support, tutorials, forums, and continuously updated documentation.
  • Real-Time Performance
    OpenCV is highly optimized for real-time applications, making it suitable for performance-critical tasks in various industries such as robotics and interactive installations.
  • Extensive Integration
    OpenCV can easily be integrated with other libraries and frameworks such as TensorFlow, PyTorch, and OpenCL, enhancing its capabilities in deep learning and GPU acceleration.
  • Rich Collection of examples
    OpenCV provides a large number of example codes and sample applications, which can significantly reduce the learning curve for beginners.

Possible disadvantages of OpenCV

  • Steep Learning Curve
    Due to the vast array of functionalities and the complexity of some of its advanced features, beginners may find it challenging to learn and use effectively.
  • Documentation Gaps
    While the documentation is extensive, it can sometimes be incomplete or outdated, requiring users to rely on community forums or external sources for solutions.
  • Resource Intensive
    Some functions and algorithms in OpenCV can be quite resource-intensive, requiring significant processing power and memory, which can be a limitation for low-end devices.
  • Limited High-Level Abstractions
    OpenCV provides a wealth of low-level functions, but it may lack higher-level abstractions and frameworks, necessitating more hands-on coding and algorithm development.
  • Dependency Management
    Setting up and managing dependencies can be cumbersome, especially when integrating OpenCV with other libraries or on certain operating systems.
  • Backward Compatibility Issues
    With frequent updates and new versions, backward compatibility can sometimes be problematic, potentially breaking existing code when updating.

Google ARCore videos

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OpenCV videos

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AI Courses by OpenCV.org

More videos:

  • Review - Practical Python and OpenCV

Category Popularity

0-100% (relative to Google ARCore and OpenCV)

Google ARCore

OpenCV

Augmented Reality
100 100%
Augmented Reality
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Development
100 100%
Development
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Google ARCore and OpenCV

Google ARCore Reviews

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OpenCV Reviews

7 Best Computer Vision Development Libraries in 2024
From the widespread adoption of OpenCV with its extensive algorithmic support to TensorFlow's role in machine learning-driven applications, these libraries play a vital role in real-world applications such as object detection, facial recognition, and image segmentation.
Source: www.labellerr.com
10 Python Libraries for Computer Vision
OpenCV is the go-to library for computer vision tasks. It boasts a vast collection of algorithms and functions that facilitate tasks such as image and video processing, feature extraction, object detection, and more. Its simple interface, extensive documentation, and compatibility with various platforms make it a preferred choice for both beginners and experts in the field.
Source: clouddevs.com
Top 8 Alternatives to OpenCV for Computer Vision and Image Processing
OpenCV is an open-source computer vision and machine learning software library that was first released in 2000. It was initially developed by Intel, and now it is maintained by the OpenCV Foundation. OpenCV provides a set of tools and software development kits (SDKs) that help developers create computer vision applications. It is written in C++, but it supports several...
Source: www.uubyte.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
These are some of the most basic operations that can be performed with the OpenCV on an image. Apart from this, OpenCV can perform operations such as Image Segmentation, Face Detection, Object Detection, 3-D reconstruction, feature extraction as well.
Source: neptune.ai
5 Ultimate Python Libraries for Image Processing
Pillow is an image processing library for Python derived from the PIL or the Python Imaging Library. Although it is not as powerful and fast as openCV it can be used for simple image manipulation works like cropping, resizing, rotating and greyscaling the image. Another benefit is that it can be used without NumPy and Matplotlib.
Source: towardsdatascience.com

Social recommendations and mentions

Based on our record, OpenCV should be more popular than Google ARCore. It has been mentiond 60 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Google ARCore mentions (9)

  • We're accelerating the Android XR platform with a new agreement with HTC
    - There was the AR (https://developers.google.com/ar). - Source: Hacker News / 4 months ago
  • App to get height of object.
    I don't know houw you would do it on ios but you should be able to do it on android if the phone supports it with.this library from google: https://developers.google.com/ar. Source: almost 2 years ago
  • Tracking of an exact point of an object
    If you have any control on the choice of the source/webcam, I'd recommend using a camera that can sense depth from the start (lidar cameras, like Intel RealSense if you are building something like a commercial robot; or a consumer device with lidar capabilities like iPad Pros since 2020, because they come with SDKs to do what you want from the start. E.g. https://developer.apple.com/augmented-reality/arkit/ or... Source: about 3 years ago
  • Is it possible to run an AR application on a raspberry pi 4 Model B
    You guys are right that Unity doesn't support building for arm64 Linux. It looks like the op could potentially install Android on the Raspberry Pi, which may allow them to run Android APKs built with Unity. However, AR Core is needed in order for Unity's AR functionality to work, and I suspect it would take additional work to get AR Core working on the Pi with an external camera and gyroscope. Source: about 3 years ago
  • Is Arcore required to build ar apps with unity?
    If the phone doesn't support ARCore, then you would have to implement all of the world / surface detection yourself inside your application code, which is very difficult problem to solve. Source: over 3 years ago
View more

OpenCV mentions (60)

  • Grasping Computer Vision Fundamentals Using Python
    To aspiring innovators: Dive into open-source frameworks like OpenCV or PyTorch, experiment with custom object detection models, or contribute to projects tackling bias mitigation in training datasets. Computer vision isn’t just a tool, it’s a bridge between the physical and digital worlds, inviting collaborative solutions to global challenges. The next frontier? Systems that don’t just interpret visuals, but... - Source: dev.to / about 17 hours ago
  • Top Programming Languages for AI Development in 2025
    Ideal For: Computer vision, NLP, deep learning, and machine learning. - Source: dev.to / 14 days ago
  • Why 2024 Was the Best Year for Visual AI (So Far)
    Almost everyone has heard of libraries like OpenCV, Pytorch, and Torchvision. But there have been incredible leaps and bounds in other libraries to help support new tasks that have helped push research even further. It would be impossible to thank each and every project and the thousands of contributors who have helped make the entire community better. MedSAM2 has been helping bring the awesomeness of SAM2 to the... - Source: dev.to / 5 months ago
  • 20 Open Source Tools I Recommend to Build, Share, and Run AI Projects
    OpenCV is an open-source computer vision and machine learning software library that allows users to perform various ML tasks, from processing images and videos to identifying objects, faces, or handwriting. Besides object detection, this platform can also be used for complex computer vision tasks like Geometry-based monocular or stereo computer vision. - Source: dev.to / 6 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library is used for image and video processing, offering functions for tasks like object detection, filtering, and transformations in computer vision. - Source: dev.to / 7 months ago
View more

What are some alternatives?

When comparing Google ARCore and OpenCV, you can also consider the following products

Apple ARKit - A framework to create Augmented Reality experiences for iOS

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Vuforia SDK - Vuforia is a vision-based augmented reality software platform.

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

ARToolKit - The world's most widely used tracking library for augmented reality.

NumPy - NumPy is the fundamental package for scientific computing with Python

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