Develop Reference

Unity VR How to focus eyes correctly

I am making an Android app for Unity, using my own VR engine. It is small, and I have got looking around working perfectly. The only problem I am experiencing is where I cannot get my eyes to focus on the objects in front of them. I get double vision where my left eye sees objects too far to the left, and right eye to the right. I have tried pointing the eye cameras slightly inwards and moving them based on a raycast to find out where they are looking.
I am guessing it could be something to do with pointing the eyes outwards, my rig - nintendo labo headset with android phone inside [ making do with what I've got ;) ] - unfortunately the phone and lenses don't quite line up but this doesn't seem to affect one of my other projects, or perhaps I need to distort my camera in a special way.
Honestly, I have no idea! Some help from an expert or anyone who is slightly clued up in the subject would be greatly appreciated :D

It turns out I literally just need to point the cameras out rather than in

Real time mark recognition on Android

I'm building an Android app that has to identify, in realtime, a mark/pattern which will be on the four corners of a visiting card. I'm using a preview stream of the rear camera of the phone as input.
I want to overlay a small circle on the screen where the mark is present. This is similar to how reference dots will be shown on screen by a QR reader at the corner points of the QR code preview.
I'm aware about how to get the frames from camera using native Android SDK, but I have no clue about the processing which needs to be done and optimization for real time detection. I tried messing around with OpenCV and there seems to be a bit of lag in its preview frames.
So I'm trying to write a native algorithm usint raw pixel values from the frame. Is this advisable? The mark/pattern will always be the same in my case. Please guide me with the algorithm to use to find the pattern.
The below image shows my pattern along with some details (ratios) about the same (same as the one used in QR, but I'm having it at 4 corners instead of 3)
I think one approach is to find black and white pixels in the ratio mentioned below to detect the mark and find coordinates of its center, but I have no idea how to code it in Android. I looking forward for an optimized approach for real-time recognition and display.
Any help is much appreciated! Thanks

Detecting patterns on four corners of a visiting card:
Assuming background is white, you can simply try this method.
Needs to be done and optimization for real time detection:
Yes, you need OpenCV
Here is an example of real-time marker detection on Google Glass using OpenCV
In this example, image showing in tablet has delay (blutooth), Google Glass preview is much faster than that of tablet. But, still have lag.

Unity Dev for Gear VR - Equirectangular Panorama as skybox/sphere

I've been trying to make a 360 photo viewer similar to the Oculus 360 Photos app. The only problem is when projecting onto a sphere with inverted normals, the image "warps" or "bends" as the sphere does, and results in straight lines such as door frames turning into bending images; bad result.
Changing the size of the sphere does nothing, and obviously the picture has to bend somewhere to fit onto the inner surface of the sphere, so I don't think this solution will work.
I then tried turning the photo into a cylindrical skybox, and using it as a skybox component of the camera, which works great: no bending lines, everything looks as desired. Except for one thing: there is a shimmering/aliasing effect on the texture, unless I enable mip maps, which then results in a blurred image.
Does anybody know how I could apply my image to appear similar to those in the Oculus 360 Photo app? They render with perfect quality and no bending lines, no shimmering. How do they achieve this result?
I've tried different compression types and different shapes, the only thing I haven't tried is slicing the photo into 6 pieces and rendering it on the inside of a cube around the camera, which, due to it's proximity, might not get the shimmery result that could be cause by distance from the camera?
Thoughts, suggestions, questions? Any assistance or discussion is appreciated

I was able to get good results by increasing the renderscale to 1.5 or higher, which eliminated the shimmery aliasing effect. Not 100% sure if this was an issue due to the Samsung s6 resolution, but I just work now with an enhanced render scale for higher quality regardless, and optimise elsewhere to save on framerate

I know that question is old now but I had that problem too but on the oculus go and it is solved thanks to these instructions here & here

Unity3d ARTookit5 Blurred camera on android mobile

i'm trying to do a simple AR scene with NFT image that i've created with genTextData. The result works fairly well in unity editor, but once compiled and run on an android device, the camera resolution is very bad and there's no focus at all.
My marker is rather small (3 cm picture), and the camera is so blurred that the AR cannot identify the marker from far away. I have to put the phone right in front of it (still verrrrryy blurred) and it will show my object but with a lot of flickering and jittering.
I tried playing with the filter fields (Sample rate/cutoff..), it helped just a little bit wit the flickering of the object, but it would never display it from far away..i always have to put my phone like right in front of it. The result that i want should be: detecting the small marker (sharp resolution or/and good focus) from a fair distance away from it..just like the distance from your computer screen to your eyes.
The problem could be camera resolution and focus, or it could be something else. But i'm pretty sure that the AR cannot identify the marker points because of the blurriness.
Any ideas or solutions about this problem ?

You can have a look here:
http://augmentmy.world/augmented-reality-unity-games-artoolkit-video-resolution-autofocus
I compiled the Unity plugin java part and set it to use the highest resolution from your phone. Also the auto focus mode is activated.
Tell me if that helps.

Marker Recognition on Android (recognising Rubik's Cubes)

I'm developing an augmented reality application for Android that uses the phone's camera to recognise the arrangement of the coloured squares on each face of a Rubik's Cube.
One thing that I am unsure about is how exactly I would go about detecting and recognising the coloured squares on each face of the cube. If you look at a Rubik's Cube then you can see that each square is one of six possible colours with a thin black border. This lead me to think that it should be relativly simply to detect a square, possibly using an existing marker detection API.
My question is really, has anybody here had any experience with image recognition and Android? Ideally I'd like to be able to implement and existing API, but it would be an interesting project to do from scratch if somebody could point me in the right direction to get started.
Many thanks in advance.

Do you want to point the camera at a cube, and have it understand the configuration?
Recognizing objects in photographs is an open AI problem. So you'll need to constrain the problem quite a bit to get any traction on it. I suggest starting with something like:
The cube will be photographed from a distance of exactly 12 inches, with a 100W light source directly behind the camera. The cube will be set diagonally so it presents exactly 3 faces, with a corner in the center. The camera will be positioned so that it focuses directly on the cube corner in the center.
A picture will taken. Then the cube will be turned 180 degrees vertically and horizontally, so that the other three faces are visible. A second picture will be taken. Since you know exactly where each face is expected to be, grab a few pixels from each region, and assume that is the color of that square. Remember that the cube will usually be scrambled, not uniform as shown in the picture here. So you always have to look at 9*6 = 54 little squares to get the color of each one.
The information in those two pictures defines the cube configuration. Generate an image of the cube in the same configuration, and allow the user to confirm or correct it.
It might be simpler to take 6 pictures - one of each face, and travel around the faces in well-defined order. Remember that the center square of each face does not move, and defines the correct color for that face.
Once you have the configuration, you can use OpenGL operations to rotate the cube slices. This will be a program with hundreds of lines of code to define and rotate the cube, plus whatever you do for image recognition.

In addition to what Peter said, it is probably best to overlay guide lines on the picture of the cube as the user takes the pictures. The user then lines up the cube within the guide lines, whether its a single side (a square guide line) or three sides (three squares in perspective). You also might want to have the user specify the number of colored boxes in each row. In your code, sample the color in what should be the center of each colored box and compare it to the other colored boxes (within some tolerance level) to identify the colors. In addition to providing the recognized results to the user, it would be nice to allow the user to make changes to the recognized colors. It does not seem like fancy image recognition is needed.

Nice idea, I'm planing to use computer vision and marker detectors too, but for another project. I am still looking if there is any available information on the web, ex: linking openCV or ARtoolkit to the Android SDK. If you have any additional information, about how to link a computer vision API, please let me know.
See you soon and goodluck!

NYARToolkit uses marker detection and is made in JAVA (as well as managed C# for windows devices). I don't know how well it works on the android platform, but I have seen it used on windows mobile devices, and its very well done.
Good luck, and happy programming!

I'd suggest looking at the Andoid OpenCV library. You probably want to examine the blob detection algorithms. You may also want to consider Hough lines or Countours to detect quads.