This is probably an insanely hard question. So far ARKit works with 3D models which are built in 3d modelling software. I was wondering if there was a way to use iPhone camera to scan 3d object (let's say a car), then use it in ARKit.
Any open source projects available which do this on other platforms or iOS?
You are looking for software in the "photogrammetry" category. There are various software tools that will stitch your photos into 3D models, but one option is Autodesk Remake. There is a free version.
ARKit/RealityKit on iPad/iPhone with a LiDAR scanner let you reconstruct a current scene and obtain a 3D geometry with an Occlusion Material applied. This geometry allows you occlude any object including a human being and physically "interact" with this generated mesh. LiDAR's working distance is up to 5 meters.
However, scanning a car isn't a good idea due to paint's high reflectivity.
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.
I know that ARKit is able to detect and classify planes on A12+ processors. It does the job reasonably well inside the house, but what about the outside? Is it able to detect windows and doors if I move around a house a little? I tried it myself and the result did not satisfy me: i moved around the building too much and still ARKit did not distinguish wall from the window.
I used app from here for tests: https://developer.apple.com/documentation/arkit/tracking_and_visualizing_planes
I’m I doing everything correct? Maybe there is some third party library to detect house parts better?
Thanks in advance!
When you test the sample app outside and try to use ARKit to detect the surfaces on the exterior of a house it will not work. ARKit is built to map flat surfaces and their orientations (horizontal/vertical). This means ARKit can understand that a surface is flat, is either a wall or a floor. When you attempt to "map" the exterior of a house, ARKit will only detect the horizontal surfaces as walls, it cannot distinguish between walls and windows.
You will need to develop/source an AI model and run it against the camera data using CoreML to enable your app to distinguish between windows and walls on the exterior of a house.
ARKit Plane tracking documentation for reference: https://developer.apple.com/documentation/arkit/tracking_and_visualizing_planes
a couple articles about ARKit with CoreML
https://www.rightpoint.com/rplabs/dev/arkit-and-coreml
https://medium.com/s23nyc-tech/using-machine-learning-and-coreml-to-control-arkit-24241c894e3b
[Update]
Yes you are correct, for A12+ devices Apple does allow for plane classification. I would assume the issue with exterior windows vs interior is either distance to the window (too far for the CV to properly classify) or Apple has tuned it more for interior windows vs exterior. The difference may seem trivial but to a CV algorithm it's quite different.
I want to develop superimposition-based AR app (the device recognises a real world 3d object and overlays AR content) for manufacturing and/or automobile industry.
How to develop such kind of apps in iOS? Are there available apps using iOS or any other technology/platform?
For instance: if a car get detected then it will provide information about all parts of it as we rotate an object.
In ARKit framework you can use two approaches to accomplish industrial augmentation:
Image detection technique. For this, you need to take a series of photos of an object from many different points of view. For me it's a less CPU/GPU intensive method.
3D object detection technique. It's CPU/GPU intensive method. It also requires from you more complex implementation.
Additionally, you may use Vision and CoreML frameworks for Image Recognition.
Hope this helps.
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.
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.