I have a stereo camera setup and I am trying to generate a 3d reconstruction of the scene. I am able to obtain the disparity map from the images. After getting the disparity map, I am unable to understand how to get the scene. I am following this tutorial given by MATLAB. Can anyone please help me with it?
I have uploaded all my files here.
Typically, the depth value is on the Z coordinate. From this disparity map, and taking in consideration the tutorial link you provided, in order to get the objects depth, you just need to know their 3D position. Once you know the actual 3D (X,Y,Z), you just need to look at the Z coordinate.
Tutorial Section - Stitch a Sequence of Point Clouds
hScatter.ZData = ptCloudScene.Location(:,3);
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I'd like to find a transformation that projects the image from the Left camera onto the image from the Right camera so that the two become aligned. I already managed to do that with two similar cameras (IGB and RGB), by using the disparity map and shifting each pixel by the corresponding disparity value. My problem is that this doesn't work for other cameras that I'm using (for example multispectral and infrared sensors), because the calculated disparity maps have very little detail. I am currently using the Matlab Computer Vision Tool Box, and I suspect that the problem is the poor correlation of information in the images (little correspondences found by the disparity algorithm).
I would like to know if there is another way of doing this transformation, for example just by using the Extrinsic and Intrinsic Parameters of the cameras (the are already calibrated).
The disparity IS the transformation you are looking for. Any sensible left-right mapping depends on 3D information, which the disparity provides. Anything else is just hallucinating some values based on assumptions that may or may not make sense.
I have 512x512x313 volume of dicom images and i have a point represented in world coordinates say (57.7475 63.4184 83.1515), how could I get the corresponding pixel coordinate of that world coordinate in Matlab??
I hate to burst your bubble, but what you are asking for is impossible. The only way that I can think of where you are able to get a correspondence between real-world co-ordinates and pixel co-ordinates is if you calibrate the camera that was used to capture the images. Once you know the intrinsic and extrinsic parameters, you then have a transformation matrix that can map real-world co-ordinates to pixel co-ordinates.
I'm assuming you don't have calibration information for your camera, and so an alternative approach would be knowing which pixels in your image map to which real-world co-ordinates. You would need to know point correspondences between those points that map between the real-world and to your image. Once you know this, you would then compute the camera transformation matrix through least-squares and then you would use this matrix to determine which points map from the real-world to your image.
Unless you have pixel correspondences to each of your real-world co-ordinates, it is impossible to do what you're asking.
FWIW, if you want to see the procedure on how to obtain the transformation matrix, check out these notes: http://www.peterhillman.org.uk/downloads/whitepapers/calibration.pdf. This was a great starting point for me when I started learning about camera calibration. Take a look at Section #5 (Page #8), as this is what I believe you are looking for.... but you will need to have correspondences between the real-world co-ordinates and your image.
Good luck!
Using Stereo vision and based on Multiple View Geometry book (http://www.robots.ox.ac.uk/~vgg/hzbook/), I have created a 3D point cloud in MATLAB. To do that, I first calibrated the cameras and rectified the stereo images. Then feature extraction and matching. Then eliminated the noisy matched based on camera locations. Finally created the 3D point cloud using triangulation.
Now my question is how to convert this 3D point cloud from pixel domain to actual millimeter/centimeter domain knowing my focal length and camera calibration matrices?
the goal is to find DEPTH IN MILLIMETERS.
I know how to do it in disparity/depth map case using formula: Z=(t*f)/d.
But here in the sparse case, can I do something like this? http://matlab.wikia.com/wiki/FAQ#How_do_I_measure_a_distance_or_area_in_real_world_units_instead_of_in_pixels.3F
or there is a more sophisticated method with more in depth explanation?
Thanks.
The formula you wrote is valid only in the special case when the image planes of the two cameras are on the same geometrical plane, and the motion from one to the other is a translation parallel to one of the image axes.
In the general case you'll need to triangulate actual rays in 3D space, using one of the techniques described in that book (it has a whole chapter on reconstruction). The reconstruction will be metrical if your calibration is. In particular, if the coordinate transform between the cameras has a translation vector whose units are meters (or millimeters, or inches, ...).
I copied the matlab code from: http://www.mathworks.fr/fr/help/vision/ug/stereo-image-rectification.html
I can compute the 3D coordinates but I am not sure if it is the correct one.
Starting from the disparity map and calculating the 3D coordinates, how do we take into account of the warping tform1 and tform2?
The problem here is that you are using uncalibrated cameras. In this case you can get up-to-scale reconstruction, but if you want the 3D points in world units, you would need to know actual distances to some points in the world.
I think you would be better off calibrating your stereo system. Please see this example.
I am doing a project in Matlab on Image processing
Is there any possibility of getting 3d image from 2d image?
If you have multiple images of the same object and the position of the camera when the picure was taken, then it is possible, but still not easy. You can find two such datasets and links to relevant articles here: http://vision.middlebury.edu/mview/
a 3d image would be a projection from 4d (and to show one of those you've got to project down to 2d) and most images that can be displayed on computer or in a picture frame are 2d projections of 3d objects due to this projection which in fact selects a slice of the higher dimensional space it doesn't contain the information needed to invert that projection and get back to 3d from a 2d image
but if you have sufficient sampling of the space it is possible to reconstruct a 3d object from 2d images of it but i don't know of any simple ways to do this
You can't do this without supporting data such as multiple 2D images describing the same 3D object. You then need to figure out the perspectives from which each image was taken, reconcile those into real space, and generate your points using a method such as intersection of stereo lines through each image plane onto the same physical coordinate.
You can also attempt a superpixel approach by exploiting lighting data within a single image, though these methods aren't as accurate.
This is a big field.
The Radon transform is used in tomography applications to reconstruct 3D representations (i.e.images) from many 2D projections of the 3D "scene". This transform and its inverse are present in the image processing toolbox of Matlab. You might want to have a look at it.
Hope this helps.
A.