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Question

I am working on building 3D point cloud from features matching using OpenCV3.1 and OpenGL.

I have implemented 1) Camera Calibration (Hence I am having Intrinsic Matrix of the camera) 2) Feature extraction( Hence I have 2D points in Pixel Coordinates). I was going through few websites but generally all have suggested the flow for converting 3D object points to pixel points but I am doing completely backword projection. Here is the ppt that explains it well.

I have implemented film coordinates(u,v) from pixel coordinates(x,y)(With the help of intrisic matrix). Can anyone shed the light on how I can render "Z" of camera coordinate(X,Y,Z) from the film coordinate(x,y).

Please guide me on how I can utilize functions for the desired goal in OpenCV like solvePnP, recoverPose, findFundamentalMat, findEssentialMat.

Answer 1

You can't, if all you have is 2D images from that single camera location.

In theory you could use heuristics to infer a Z stacking. But mathematically your problem is under defined and there's literally infinitely many different Z coordinates that would evaluate your constraints. You have to supply some extra information. For example you could move your camera around over several frames (Google "structure from motion") or you could use multiple cameras or use a camera that has a depth sensor and gives you complete XYZ tuples (Kinect or similar).

Update due to comment:

For every pixel in a 2D image there is an infinite number of points that is projected to it. The technical term for that is called a ray . If you have two 2D images of about the same volume of space each image's set of ray (one for each pixel) intersects with the set of rays corresponding to the other image. Which is to say, that if you determine the ray for a pixel in image #1 this maps to a line of pixels covered by that ray in image #2. Selecting a particular pixel along that line in image #2 will give you the XYZ tuple for that point.

Since you're rotating the object by a certain angle θ along a certain axis a between images, you actually have a lot of images to work with. All you have to do is deriving the camera location by an additional transformation ( inverse(translate(-a)·rotate(θ)·translate(a) ).

Then do the following: Select a image to start with. For the particular pixel you're interested in determine the ray it corresponds to. For that simply assume two Z values for the pixel. 0 and 1 work just fine. Transform them back into the space of your object, then project them into the view space of the next camera you chose to use; the result will be two points in the image plane (possibly outside the limits of the actual image, but that's not a problem). These two points define a line within that second image. Find the pixel along that line that matches the pixel on the first image you selected and project that back into the space as done with the first image. Due to numerical round-off errors you're not going to get a perfect intersection of the rays in 3D space, so find the point where the ray are the closest with each other (this involves solving a quadratic polynomial, which is trivial).

To select which pixel you want to match between images you can use some feature motion tracking algorithm, as used in video compression or similar. The basic idea is, that for every pixel a correlation of its surroundings is performed with the same region in the previous image. Where the correlation peaks is, where it likely was moved from into.

With this pixel tracking in place you can then derive the structure of the object. This is essentially what structure from motion does.

Answer 2

With single camera and rotating object on fixed rotation platform I would implement something like this:

Each camera has resolution xs,ys and field of view FOV defined by two angles FOVx,FOVy so either check your camera data sheet or measure it. From that and perpendicular distance ( z ) you can convert any pixel position ( x,y ) to 3D coordinate relative to camera (x',y',z'). So first convert pixel position to angles:

ax = (x - (xs/2)) * FOVx / xs 
ay = (y - (ys/2)) * FOVy / ys 

and then compute cartesian position in 3D:

x' = distance * tan(ax)
y' = distance * tan(ay)
z' = distance

That is nice but on common image we do not know the distance . Luckily on such setup if we turn our object than any convex edge will make an maximum ax angle on the sides if crossing the perpendicular plane to camera. So check few frames and if maximal ax detected you can assume its an edge (or convex bump) of object positioned at distance .

If you also know the rotation angle ang of your platform (relative to your camera) Then you can compute the un-rotated position by using rotation formula around y axis ( Ay matrix in the link) and known platform center position relative to camera (just subbstraction befor the un-rotation)... As I mention all this is just simple geometry.

In an nutshell:

1. obtain calibration data

   FOVx,FOVy,xs,ys,distance. Some camera datasheets have only FOVx but if the pixels are rectangular you can compute the FOVy from resolution as

    FOVx/FOVy = xs/ys 

   Beware with Multi resolution camera modes the FOV can be different for each resolution !!!

2. extract the silhouette of your object in the video for each frame

   you can subbstract the background image to ease up the detection

3. obtain platform angle for each frame

   so either use IRC data or place known markers on the rotation disc and detect/interpolate...

4. detect ax maximum

   just inspect the x coordinate of the silhouette (for each y line of image separately) and if peak detected add its 3D position to your model. Let assume rotating rectangular box. Some of its frames could look like this:

   

   So inspect one horizontal line on all frames and found the maximal ax . To improve accuracy you can do a close loop regulation loop by turning the platform until peak is found "exactly". Do this for all horizontal lines separately.

   btw. if you detect no ax change over few frames that means circular shape with the same radius ... so you can handle each of such frame as ax maximum.

Easy as pie resulting in 3D point cloud. Which you can sort by platform angle to ease up conversion to mesh ... That angle can be also used as texture coordinate ...

But do not forget that you will lose some concave details that are hidden in the silhouette !!!

If this approach is not enough you can use this same setup for stereoscopic 3D reconstruction . Because each rotation behaves as new (known) camera position.