I am totally new to AR and I searched on the internet about marker based and markerless AR but I am confused with marker based and markerless AR..
Lets assume an AR app triggers AR action when it scans specific images..So is this marker based AR or markerless AR..
Isn't the image a marker?
Also to position the AR content does marker based AR use devices' accelerometer and compass as in markerless AR?
In a marker-based AR application the images (or the corresponding image descriptors) to be recognized are provided beforehand. In this case you know exactly what the application will search for while acquiring camera data (camera frames). Most of the nowadays AR apps dealing with image recognition are marker-based. Why? Because it's much more simple to detect things that are hard-coded in your app.
On the other hand, a marker-less AR application recognizes things that were not directly provided to the application beforehand. This scenario is much more difficult to implement because the recognition algorithm running in your AR application has to identify patterns, colors or some other features that may exist in camera frames. For example if your algorithm is able to identify dogs, it means that the AR application will be able to trigger AR actions whenever a dog is detected in a camera frame, without you having to provide images with all the dogs in the world (this is exaggerated of course - training a database for example) when developing the application.
Long story short: in a marker-based AR application where image recognition is involved, the marker can be an image, or the corresponding descriptors (features + key points). Usually an AR marker is a black&white (square) image,a QR code for example. These markers are easily recognized and tracked => not a lot of processing power on the end-user device is needed to perform the recognition (and optionally tracking).
There is no need of an accelerometer or a compass in a marker-based app. The recognition library may be able to compute the pose matrix (rotation & translation) of the detected image relative to the camera of your device. If you know that, you know how far the recognized image is and how it is rotated relative to your device's camera. And from now on, AR begins... :)
Well. Since I got downvoted without explanation. Here is a little more detail on markerless tracking:
Actual there are several possibilities for augmented reality without "visual" markers but none of them called markerless tracking.
Showing of the virtual information can be triggered by GPS, Speech or simply turning on your phone.
Also, people tend to confuse NFT(Natural feature tracking) with markerless tracking. With NFT you can take a real life picture as a marker. But it is still a "marker".
This site has a nice overview and some examples for each marker:
Marker-Types
It's mostly in german but so beware.
What you call markerless tracking today is a technique best observed with the Hololens(and its own programming language) or the AR-Framework Kudan. Markerless Tracking doesn't find anything on his own. Instead, you can place an object at runtime somewhere in your field of view.
Markerless tracking is then used to keep this object in place. It's most likely uses a combination of sensor input and solving the SLAM( simultaneous localization and mapping) problem at runtime.
EDIT: A Little update. It seems the hololens creates its own inner geometric representation of the room. 3D-Objects are then put into that virtual room. After that, the room is kept in sync with the real world. The exact technique behind that seems to be unknown but some speculate that it is based on the Xbox Kinect technology.
Let's make it simple:
Marker-based augmented reality is when the tracked object is black-white square marker. A great example that is really easy to follow shown here: https://www.youtube.com/watch?v=PbEDkDGB-9w (you can try out by yourself)
Markerless augmented reality is when the tracked object can be anything else: picture, human body, head, eyes, hand or fingers etc. and on top of that you add virtual objects.
To sum it up, position and orientation information is the essential thing for Augmented Reality that can be provided by various sensors and methods for them. If you have that information accurate - you can create some really good AR applications.
It looks like there may be some confusion between Marker tracking and Natural Feature Tracking (NFT). A lot of AR SDK's tote their tracking as Markerless (NFT). This is still marker tracking, in that a pre-defined image or set of features is used. It's just not necessarily a black and white AR Toolkit type of marker. Vuforia, for example, uses NFT, which still requires a marker in the literal sense. Also, in the most literal sense, hand/face/body tracking is also marker tracking in that the marker is a shape. Markerless, inherent to the name, requires no pre-knowledge of the world or any shape or object be present to track.
You can read more about how Markerless tracking is achieved here, and see multiple examples of both marker-based and Markerless tracking here.
Marker based AR uses a Camera and a visual marker to determine the center, orientation and range of its spherical coordinate system. ARToolkit is the first full featured toolkit for marker based tracking.
Markerless Tracking is one of best methods for tracking currently. It performs active tracking and recognition of real environment on any type of support without using special placed markers. Allows more complex application of Augmented Reality concept.
Related
Is it possible to import a virtual lamp object into the AR scene, that projects a light cone, which illuminates the surrounding space in the room and the real objects in it, e.g. a table, floor, walls?
For ARKit, I found this SO post.
For ARCore, there is an example of relighting technique. And this source code.
I have also been suggested that post-processing can be used to brighten the whole scene.
However, these examples are from a while ago and perhaps threre is a newer or a more straight forward solution to this problem?
At the low level, RealityKit is only responsible for rendering virtual objects and overlaying them on top of the camera frame.
If you want to illuminate the real scene, you need to post-process the camera frame.
Here are some tutorials on how to do post-processing:
Tutorial1⃣️
Tutorial2⃣️
If all you need is an effect like This , then all you need to do is add a CGImage-based post-processing effect for the virtual object (lights).
More specifically, add a bloom filter to the rendered image(You can also simulate bloom filters with Gaussian blur).
In this way, the code is all around UIImage and CGImage, so it's pretty simple😎
If you want to be more realistic, consider using the depth map provided by LiDAR to calculate which areas can be illuminated for a more detailed brightness.
Or If you're a true explorer, you can use Metal to create a real world Digital Twin point cloud in real time to simulate occlusion of light.
There's nothing new in relighting techniques based on 3D compositing principles in 2021. At the moment, when you're working with RealityKit or SceneKit, you have to personally implement the relighting functionality with the help of two additional render passes (RGB pass is always needed) - Normals pass and PointPosition pass. Both AOVs must be 32-bit.
However, in the near future, when Apple engineers finally implement texture capturing in Scene Reconstruction – any inexperienced AR developer will be able to apply a relighting procedure.
Watch this Vimeo Video to find out how relighting can be achieved in The Foundry NUKE.
A crucial point here, when implementing the Relighting effect, is the presence of a LiDAR scanner (or iToF sensor if you're using ARCore). In other words, today's relighting solution for iOS is Metal + RealityKit.
Many AR applications allow user to select a 3D object and display in the phone. If the user walks into a different area. The 3D object still remains at the same location. How can i achieve this? Can GPS solve this problem? But GPS is not accurate in indoor.
You can do it by using ARKit, ARCore or Vuforia SDKs. In ARKit and ARCore you can anchor objects to physical locations and they will stay there even if you walk into a different room. However, you might notice some drift in the location in ARCore because environmental understanding of device change over time or you might lose tracking at some point. With Vuforia you can use extended tracking to track objects but it is a bit different than ARCore and ARKit. You have to use ground plane or smart terrain to utilize extended tracking fully in your situation.
ARCore can track static surfaces according to its documentation, but doesn't mention anything about moving surfaces, so I'm wondering if ARCore can track flat surfaces (of course, with enough feature points) that can move around.
Yes, you definitely can track moving surfaces and moving objects in ARCore.
If you track static surface using ARCore – the resulted features are mainly suitable for so-called Camera Tracking. If you track moving object/surface – the resulted features are mostly suitable for Object Tracking.
You also can mask moving/not-moving parts of the image and, of course, inverse Six-Degrees-Of-Freedom (translate xyz and rotate xyz) camera transform.
Watch this video to find out how they succeeded.
Yes, ARCore tracks feature points, estimates surfaces, and also allows access to the image data from the camera, so custom computer vision algorithms can be written as well.
I guess it should be possible theoretically.
However, Ive tested it with some stuff in my HOUSE (running S8 and an app with unity and arcore)
and the problem is more or less that it refuses to even start tracking movable things like books and plates etc:
due to the feature points of the surrounding floor etc it always picks up on those first.
Edit: did some more testing and i Managed to get it to track a bed sheet, it does However not adjust to any movement. Meaning as of now the plane stays fixed allthough i saw some wobbling but i guess that Was because it tried to adjust the Positioning of the plane once it's original Feature points where moved.
What are the ways to identify a particular object in a room and the position of the user for indoor navigation with AR. I understand that we can use beacon and marker to identify an object or the location of user in a room.
Without using them what are the other alternatives for finding user location and identifying an object for AR experience. I am exploring on AR for indoor navigation with iOS devices(currently focusing on using ARKit). If we use core location for user positioning, the accuracy is low. In a small shop if we use core location or any map related services we will face user/product miss positioning leading to not-a-good experience for users. Any other ways/solutions to solve this?
The obvious alternative way to detect objects visually in a scene would be to use CoreML framework with ARKit. A basic app is already available on Github.
CoreML-in-ARKit
You can also obtain the worldPosition of those objects relative to a starting origin & plot the x,z coordinate system (indoor map) based on the SCNNode label position. It’s not going to be that accurate... but it’s a basic object identification and positioning system.
Edit:
One limitation of using an out-of-the-box CoreML image classifiers like
Inceptionv3.mlmodel is it only detects the dominant generic objects from a set of generic categories such as trees, animals, food, vehicles, people, and more.
You mention doing an object recognition (image classifying) inside a retail shop. This will need a custom image classifier that can for example discriminate different types of iphone models (iphone7, iphone 8 or iphone X) rather than merely determining its a smartphone.
To create your own object recogniser (image classifier) for ARkit follow this tutorial written by Hunter Ward.
https://medium.com/#hunter.ley.ward/create-your-own-object-recognizer-ml-on-ios-7f8c09b461a1
code is available on Github:
https://github.com/hanleyweng/Gesture-Recognition-101-CoreML-ARKit
Note: If you need to create a custom classifier for 100’s of items in a retail shop... Ward recommends around 60 images per class... which would total around 60 x 100 = 6000 images. To generate the Core ML model, Ward uses a Microsoft Cognitive Service called “Custom Vision”... which currently has a limit of 1000 images. So if you need to do more than 1000 images you will have to find another way to create the model.
I would like some hints, maybe more, on detecting a custom image marker in a real-time video feed. I'm using OpenCV, iPhone and the camera feed.
By custom image marker I'm referring to a predefined image, but it can be any kind of image (not a specific designed marker). For example, it can be a picture of some skyscrapers.
I've already worked with ARTags and understand how they are detected, but how would I detect this custom image and especially find out its position & orientation?
What makes a good custom image to be detected successfully?
Thanks
The most popular markers used in AR are
AR markers (a simple form of QR codes) - those detected by AR tookit & others
QR codes. There are plenty of examples on how to create/detect/read QR.
Dot grids. Similar with the chess grids used in calibration. It seems their detection can be more robust than the classical chess grid. OpenCV has codes related to dot grid detection in the calibration part. Also, the OpenCV codebase offers a good starting point to extract 3D position and orientation.
Chess grids. Similar to dot grids. They were the standard calibration pattern, and some people used them for marker detection of a long time. But they lost their position to dot grids recently, when some people discovered that dots can be detected with better accuracy.
Note:
Grids are symmetrical. I bet you already know that. But that means you will not be able to
recover full orientation data from them. You will get the plane where the grid lies, but nothing more.
Final note:
Code and examples for the first two are easily found on the Internet. They are considered the best by many people. If you decide to use the grid patterns, you have to enjoy some math and image processing work :) And it will take more.
This answer is valid no more since Vuforia is now a paid engine.
I think you should give Vuforia a try. It's a AR engine that can use any image you want as a marker. What makes a good marker for Vuforia is high frequency images.
http://www.qualcomm.com/solutions/augmented-reality
Vuforia is a free to use engine.