AR technologies are gradually conquering the world  Large companies understand the tendency and support developers with new tools and technologies. That’s how ARCore and ML Kit appeared to the AR community. 樂 But how can these tools affect the development of AR? ☝️ ARCore offers a set of features for an uncut interaction with the environment. Motion tracking, light estimation, and environmental understanding allow developers to use AR more effectively.  As for the ML Kit, it offers more flexibility to developers with its ability to deploy projects on the cloud or on-device. On top of that, it saves the development time with premade sets for common use patterns for text recognition, face detection, and more.  A full comparison of ARCore and ML Kit.

1. ARCore
ML-Kit
vs
Augmented reality is gaining momentum. AR mobile apps are both entertaining and useful
for solving everyday tasks. Right now, we can find lots of tools to create apps with uncut AR
experience. However, there are two leading SDKs recognized by the majority of the AR
community. We’ve decided to tell you about their capabilities and pinpoint the differences
between them.
ML Kit is a mobile SDK that brings Google's on-device machine learning experience
to Android and iOS apps. ML Kit's APIs run on-device, allowing for real-time use cases.
Thus, processing a live camera stream, for example, is no longer a problem. On top of that,
the functionality is available offline.
ML Kit simplifies integrations of ML techniques in your apps by bringing Google's ML
technologies, such as the Google Cloud Vision API, TensorFlow Lite, and the Android Neural
Networks API together in a single SDK. Whether you need the power of cloud-based
processing, the real-time capabilities of mobile-optimized on-device models, or the flexibility
of custom TensorFlow Lite models, ML Kit makes it possible with just a few lines of code.
ARCore is Google’s platform for building augmented reality experiences. Using different APIs,
ARCore enables your phone to sense the environment, understand the world, and interact
with information. Some of the APIs are available across Android and iOS to enable shared AR
experiences.
ARCore uses three key features to integrate virtual content with the real world as seen
through your phone's camera:
• Motion tracking allows the phone to understand and track its position relative
to the world.
• Environmental understanding allows the phone to detect the size and location
of all types of surfaces: horizontal, vertical, and angled surfaces like the ground, a coffee table,
or walls.
• Light estimation allows the phone to estimate the environment's current lighting
conditions.
You can also see ARCore supported devices on the official website.
Main fundamental ARCore concepts:
• Motion tracking - ARCore uses SLAM technology (simultaneous localization
and mapping) to understand the phone’s position relative to the world around it;
• Environmental understanding - ARCore searches for clusters of particular points
that appear to lie on common horizontal or vertical surfaces, like tables or walls, making these
surfaces available to your app as planes;
• Light estimation - ARCore detects the environmental lighting and provides you
with the average intensity and color correction of a given camera image;
• User interaction - ARCore uses hit testing to take an (x,y) coordinate corresponding
to the phone's screen and projects a ray into the camera's view of the world, returning any
planes or feature points that the ray intersects, along with the pose of that intersection
in world space;
• Oriented point - it lets you place virtual objects on angled surfaces;
• Anchors and trackables - positioning changes as ARCore improves its understanding
of its position and its environment;
• Augmented images - this is a feature that allows you to build AR apps responding
to specific 2D images (also moving images) that can be compiled offline;
• Sharing - ARCore Cloud Anchor API lets you create collaborative apps or multiplayer
games for Android and iOS devices.
ML Kit key capabilities:
• Turn-key solution for common use cases - ML Kit comes with a set of ready-to-use APIs
for common mobile use cases: recognizing text, detecting faces, identifying landmarks,
scanning barcodes, labeling images, and identifying the text language. Simply pass in data
to the ML Kit library, and it’ll give you the information you need.
• On-device or in the cloud - ML Kit’s selection of APIs run on-device or in the cloud.
On-device APIs can process your data quickly and work even when there’s no network
connection. Cloud-based APIs, on the other hand, leverage the power of Google Cloud
Platform's machine learning technology to give you an even higher level of accuracy.
• Deployment of custom models - if ML Kit's APIs don’t match your needs, you can
always bring your own existing TensorFlow Lite models. ML Kit acts as an API layer to your
custom model, making it simpler to run and use.
ARCore ML Kit
ARCore
ML Kit
Comparing ARCore vs. ML Kit
Work with light
2D objects recognition
3D planes recognition
3D objects recognition
Face detection
Text recognition
Barcode recognition
Landmark recognition
Image labeling
Custom model inference
Depth recognition
• Light level estimation
• Intensity
• Light direction
• Shadows
• Colors
• Temperature
• Environment reflection from
virtual metal objects
• Entire face (face net)
• Nose tip
• Head center
• Left or right forehead
• .OBJ, .glTF, .FBX
• Imported animation
• Location saving
• Viewing of static or animated
models in browser
• Entire face (points, like right eye,
lips, nose, etc.)
• Face contours
• Head center
• Static images
• Moving images
Horizontal, vertical
No
No
No
• Static images
• Moving images
No
No
No
Yes
Yes
Yes
Yes
Yes
No
Android, iOS
No
Yes
Yes
No
RGB depth map
NoMulti-user mode
Work with 3D models