I want to measure the horizontal plane surface to find whether it fits the object that i am going to place. For ex. if i am going to place a cot 3D model(with fixed size) in a room using iOS 11 ARKit,
First i want to detect if that room surface is sufficient or not to place my 3D model by measuring the surface area(width and height etc.)
Second if the user tries to place it without sufficient place, i should not allow him to place the cot and show him error message.
I created a sample POC by following https://developer.apple.com/sample-code/wwdc/2017/PlacingObjects.zip using which i am able to detect the horizontal plane and place the cot. But the issue is whatever may be the surface, user is able to place the cot which shouldn't be allowed in real time.
I saw couple of demos in which they say we can measure the size of the room or a horizontal plane(https://www.curbed.com/2017/6/29/15894556/ar-measure-app-augmented-reality-ruler-measuring-tape-ios)
I am using ARKit Scenekit inorder to achieve this and i am new to AR and Scenekit. I need to know if this is doable, and if so how to achieve it.
You could estimate the size of a detected plane by inspecting its dimensions. But you shouldn't.
ARKit has plane estimation, not scene reconstruction. That is, it'll tell you there's a flat surface at (some point) and that said surface probably extends at least (some distance) from that point. It doesn't know exactly how big the surface is (it's even refining its estimate over time), and it doesn't tell you where there are interruptions in that continuous surface, much less the size and shape of such interruptions.
In fact, if you're looking at the floor and moving around, and you see one patch of floor, then another patch of floor on the other side of a solid wall from the first, ARKit will happily recognize that those two patches are coplanar and merge them into the same anchor. At the same time, neither detected patch may cover the entire extent of the floor around it.
If you attempt to restrict where the user can place virtual objects in AR based on plane estimates, you're likely to frustrate them with two kinds of error: you'll have areas where it looks to the user like they can place something but that don't allow it, and you'll have areas that look like they should be off-limits that do allow placing things.
Instead, design your experience to involve the user in deciding where the sensible places for content are. See this demo for example — ARKit detects the level of the floor (not its boundaries), then uses that to show UI indicating the size/shape of objects to be placed. It's up to the user to make sure there's enough room for the couch, etc.
As for the technical how-to on what you probably shouldn't do: The docs for ARPlaneAnchor.extent say that the x and z coordinates of that vector are the width and length of the estimated plane. And all units in ARKit are meters. (Which is width and which is length? It's a matter of perspective. And of the rotation encoded in the anchor's transform.)
Related
I am trying to build trajectory of moving camera which is seeing downwards. It works perfectly when camera is just translating, but fails when camera rotates. How to take into account of camera heading?
I am using feature matching which gives me where the specific patch is in my image and identifies the coordinates. I am tracking that patch and it give me trajectory of camera (if camera is not rotating.) But when camera rotates at a single place, it identifies the patch at the same place, and when camera starts moving it doesn't take into account of it.
For eg. if my camera is moving forward in north direction and camera is rotated to south and starts moving forward, my algorithm will not recognize it it builds trajectory just a straight line, instead of a right angle.
How to take into account of the camera rotation.
It works perfectly when camera is just translating, but fails when camera rotates. How to take into account of camera heading?
Direct approach (probably not possible)
Something must be responsible for the camera rotating. This something may know how much the camera rotates and may be able to tell you. I guess that in your case this information might not be readily available though.
Feature based image registration
A single feature is not sufficient to detect all affine transformations (translation, rotation, scaling, ..). You would need to take two features at least (for translation and rotation) or better three features (for full affine transformations) into account.
In case of two features and translation and rotation only, the center of the two features is the translation and the orientation of the connection of the two features is the rotation.
Frequency domain, intensity based image registration
Cross-correlation (via FFT) is fast in detecting translations, however, you can use this technique also for detecting rotation and scaling (see An FFT-based technique for translation, rotation, and scale-invariant image registration or Robust image registration using Log-Polar Transform)
Improved accuracy
Instead of comparing consecutive camera frames with features or intensity based techniques, compare all possible frame combinations within a certain time window (for example the time to move half a frame away), then find the single trajectory that fits all the transformations for all combinations. Computationally more expensive but more accurate.
Some words of caution
If the direct approach fails, you may be fooled by the image structures. In certain cases (uniform images, rotationally symmetric images, ...) it just won't work without an independent confirmation.
I am working on a AR based solution in which I am rendering some 3D models using SceneKit and ARKit. I have also integrated CoreML to identify objects and render corresponding 3D objects in scene.
But right now I am just rendering it in the center of screen as soon I detect the object(Only for the list of objects that I have). Is it possible to get the position of the real world object so that I can show some overlay above the object?
That is if I have a water bottled scanned, I should able to get the position of the water bottle. It could be anywhere in the water bottle but shouldn't go outside of it. Is this possible using SceneKit?
All parts of what you ask are theoretically possible, but a) for several parts, there’s no integrated API to do things for you, and b) you’re probably signing yourself up for a more difficult problem than you think.
What you presumably have with your Core ML integration is an image classifier, as that’s what most of the easy to find ML models do. Image classification answers one question: “what is this a picture of?”
What you’re looking for involves at least two additional questions:
“Given that this image has been classified as containing (some specific object), where in the 2D image is that object?”
“Given the position of a detected object in the 2D video image, where is it in the 3D space tracked by ARKit?”
Question 1 is pretty reasonable. There are models that do both classification and detection (location/bounds within an image) in the ML community. Probably the best known one is YOLO — here’s a blog post about using it with Core ML.
Question 2 is the “research team and five years” part. You’ll notice in the YOLO papers that it gives you only coarse bounding boxes for detected objects — that is, it’s working in 2D image space, not doing 3D scene reconstruction.
To really know the shape, or even the 3D bounding box of an object means integrating object detection with scene reconstruction. For example, if an object has some height in the 2D image, are you looking at a 3D object that’s tall with a small footprint, or one that’s long and low, receding into the distance? Such integration would require taking apart the inner workings of ARKit, which nobody outside Apple can do, or recreating an ARKit-alike from scratch.
There might be some assumptions you can make to get very rough estimates of 3D shape from a 2D bounding box, though. For example, if you do AR hit tests on the lower corners of a box and find that they’re on a horizontal plane, you can guess that the 2D height of the box is proportional to the 3D height of the object, and that its footprint on the plane is proportional to the box’s width. You’d have to do some research and testing to see if assumptions like that hold up, especially in whatever use cases your app covers.
I am working on an application with ARKit and SceneKit frameworks. In my application I have enabled surface detection (I followed the placing objects sample provided by Apple). How to find if the surface detected is no more available? That is, initially only if user has detected the surface in ARSession I am allowing him to place the 3D object.
But if the user moves rapidly or focuses somewhere, the detected surface area is getting lost. In this case if the user tries to place another object I shouldn't allow him to place it until he scans the floor again and get the surface corrected.
Is there any delegate which is available to let us know that the surface detected is no more available?
There are delegate functions that you can use. The delegate is the ARSCNViewDelegate
It has a function that is renderer(_:didRemove:for:) that fires when an ARAnchor has been removed. You can use this function to perform some operation when a surface gets removed.
ARSCNViewDelegate Link
There are two ways to “lose” a surface, so there’s more than one approach to dealing with such a problem.
As noted in the other answer, there’s an ARSCNViewDelegate method that ARKit calls when an anchor is removed from the AR session. However, ARKit doesn’t remove plane anchors during a running session — once it’s detected a plane, it assumes the plane is always there. So that method gets called only if:
You remove the plane anchor directly by passing it to session.remove(anchor:), or
You reset the session by running it again with the .removeExistingAnchors option.
I’m not sure the former is a good idea, but the latter is important to handle, so you probably want your delegate to handle it well.
You can also “lose” a surface by having it pass out of view — for example, ARKit detects a table, and then the user turns around so the camera isn’t pointed at or near the table anymore.
ARKit itself doesn’t offer you any help for dealing with this problem. It gives you all the info you need to do the math yourself, though. You get the plane anchor’s position, orientation, and size, so you can calculate its four corner points. And you get the camera’s projection matrix, so you can check for whether any point is in the viewing frustum.
Since you’re already using SceneKit, though, there are also ways to get SceneKit to do the math for you... Working backwards:
SceneKit gives you an isNode(_:insideFrustumOf:) test, so if you have a SCNNode whose bounding box matches the extent of your plane anchor, you can pass that along with the camera (view.pointOfView) to find out if the node is visible.
To get a node whose bounding box matches a plane anchor, implement the ARSCNViewDelegate didAdd and didUpdate callbacks to create/update an SCNPlane whose position and dimensions match the ARPlaneAnchor’s center and extent. (Don’t forget to flip the plane sideways, since SCNPlane is vertically oriented by default.)
If you don’t want that plane visible in the AR view, set its materials to be transparent.
I am trying to do my own blob detection who will receive a real time video, and try to detect a white paper sheet.
Even if is something written inside the paper. I need to detect the paper and is corner, because what i really want is to draw a opengl polygon over the paper in each corner of the paper will be a corner of the polygon. Then i need the coordinates of the paper to do other stuffs.
So i need to:
- detect a square white blob.
- get the coordinates of the cornes
- draw a polygon over the white sheet.
Any ideias how can i do that?
Much depends on context. For example, suppose that you:
know that the paper is always roughly centered (i.e. W/2, Y/2 is always inside the blob), and no more rotated than 45 degrees (30 would be better)
have a suitable border around the sheet so that the corners never touch the edges of the FOV
are able (through analysis of local variance, or if you're lucky, check of background color or luminance) to say whether a point is inside or outside the blob
the inside/outside function never fails (except possibly in the close vicinity of a border)
then you could walk a line from a point on the border (surely outside) and the center (surely inside), even through bisection, and find a point - an areal - on the edge.
Two edge points give a rect (two areals give a beam), two rects give an intersection (two beams give a larger areal) - and there's your corner. You should carry along the detection uncertainty (areal radius) in order to validate corners (another less elegant approach is to roughly calculate where the corner is, and pinpoint it with a spiral search or drunkard's walk).
This algorithm is amenable to parallelization and, as long as the hypotheses hold, should be really fast.
All that said, it remains a hack -- I agree with unwind, why reinvent the wheel? If you have memory or CPU constraints (embedded systems, etc.), I believe there ought to be OpenCV and e-Vision "lite" ports also for ARM and embedded platforms.
(Sorry for my terminology - I'm monkey-translating from Italian. "Areal" is likely to correspond to your "blob", a beam is the family of lines joining all couples of points in two different blobs, line intensity being the product of distance from a point from its areal's center)
I am trying to do my own blob detection who will receive a real time video, and try to detect a white paper sheet.
Your first shot could be a simple flood-fill. That is, select a good threshold to binarize the image and apply the algorithm. The threshold can be fixed if you know the paper is always brighter than X and the background is always darker than this. Or this can be an adaptive threshold, for example Otsu's method. OpenCV offers this for free.
If you'd need to speed it up you could use a union-find data structure.
Finally you'd need to come up with some heuristic how to identify the corners (e.g. the four extreme values in x/y direction).
Then i need [...] the coordinates of the cornes [...]
Then you don't need blob detection, but corner detection or contour detection in the first place. OpenCV has some nice functionality for exactly this.
If you can't use it, I would suggest to binarize the image as above and use a harris-detector to find the corners of the object.
OpenCV's TBB support could also come quite handy if you'd use it and you have problems to meet your real-time requirements.
here are my questions. I heard that opengl ignores the vertices which are outside the viewing frustum and doesn't consider them in rendering pipeline. Recently I ran into a same post that said you should check this your self and if a point is not inside, it is you duty to find out not opengl's! Now,
Is this true about opengl? does it understand if a point is not inside, and not to render it?
I am developing a grass scene which has about 4000 grasses on rectangles. I have awful FPS, and the only solution I came up was to decide which grasses are inside the viewport and then only render them! My question here is that what solution is best for me to find out which rectangle is not inside or which one is?
Please consider that my question is not about points mainly but about rectangles. Also I need to sort the grasses based on their distance, so it is better if native on client side memory.
Please let me know if there are any effective and real-time ways to find out if any given mesh is inside or outside the frustum. Thanks.
Even if is true then OpenGL does not show polygons outside the frustum ( as any other 3d engines ) it has to consider them to check if there are inside or not and then fps slow down. Usually some smart optimization algorithm is needed to avoid flooding the scene with invisible objects. Check for example BSP trees+PVS or Portals as a starting point.
To check if there is some bottleneck in the application, you can try with gDebugger. If nothing is reasonable wrong optimizing in order to draw just the PVS ( possible visible set ) is the way to go.
OpenGL won't render pixels ("fragments") outside your screen, so it has to clip somehow...
More precisely :
You submit your geometry
You make a Draw Call (glDrawArrays or glDrawElements)
Each vertex goes through the vertex shader, which computes the final position of the vertex in camera space. If you didn't write a vertex shader (=old opengl), the driver will create one for you.
The perspective division transforms these coordinates in Normalized Device Coordinates. Roughly, its means that the frustum of your camera is deformed to fit in a [-1,1]x[-1,1]x[-1,1] box
Everything outside this box is clipped. This can mean completely discarding a triangle, or subdivide it if it is across a clipping plane
Each remaining triangle is rasterized into fragments
Each fragment goes through the fragment shader
So basically, OpenGL knows how to clip, but each vertex still has to go through the vertex shader. So submitting your entire world will work, of course, but if you can find a way not to submit everything, your GPU will be happier.
This is a tradeoff, of course. If you spend 10ms checking each and every patch of grass on the CPU so that the GPU has only the minimal amount of data to draw, it's not a good solution either.
If you want to optimize grass, I suggest culling big patches (5m x 5m or so). It's standard AABB-frustum testing.
If you want to optimize a more generic model, you can investigate quadtree for "flat" models, octrees and bsp-trees for more complex objects.
Yes, OpenGL does not rasterize triangles outsize the viewing frustrum. But, this doesn't mean that this is optimal for applications: OpenGL implementation shall transform the vertex coordinate (by using fixed pipeline or vertex shaders), then, having the normalized coordinates it finally knows whether the triangle lie inside the viewing frustrum.
This mean that no pixel is rasterized in that cases, but the vertex data is processed all the same; simply doesn't produce fragments derived from a non visible triangle!
The OpenGL extension ARB_occlusion_query may help you, but in the discussion section make it clear:
Do occlusion queries make other visibility algorithms obsolete?
No.
Occlusion queries are helpful, but they are not a cure-all. They
should be only one of many items in your bag of tricks to decide
whether objects are visible or invisible. They are not an excuse
to skip frustum culling, or precomputing visibility using portals
for static environments, or other standard visibility techniques.
For the question regarding the mesh sorting on depth, you shall use the depth buffer: essentially the mesh fragment is effectively rendered only if its distance from the viewport is less than the previous fragment in the same position. This make you aware of sorting meshes. This buffer is essentially free, and it allows you to improve performances since it discard more far fragments.
Yes. Like others have pointed out, OpenGL has to perform a lot of per-vertex operations to determine if it is in the frustum. It must do this for every vertex you send it. In addition to the processing overhead that must take place, keep in mind that there is also additional overhead in the transmission of those vertices from the CPU to the GPU. You want to avoid sending information to the GPU that it isn't going to use. Though the bandwidth between the CPU and GPU is quite good on modern hardware, there's still a limit.
What you want is a Scene Graph. Scene graphs are frequently implemented with some kind of spatial partitioning scheme, e.g., Quadtrees, Octrees, BSPTrees, etc etc. Spatial partitioning allows you to intelligently determine what geometries are visible. Instead of doing this on a per-vertex basis (like OpenGL is forced to do) it can eliminate huge spatial subsets of geometry at a time. When rendering a complex scene, the performance savings can be enormous.