I am trying to perform a 3D surface reconstruction from a stereo configuration with OpenCV example files. I have created a stereo camera from 2 web cams.
I have obtained the calibration parameters using stereo_calib.cpp ( https://code.ros.org/trac/opencv/browser/trunk/opencv/samples/cpp/stereo_calib.cpp?rev=4086 ) and generated a point cloud with stereo_match.cpp ( https://code.ros.org/trac/opencv/browser/trunk/opencv/samples/c/stereo_match.cpp?rev=2614 ). The resulting point cloud, opened with MeshLab doesn't resemble the original scene at all ( http://img707.imageshack.us/i/snapshot01u.png/ ).
What am I missing here?
Steps to recreate:
stereo_calib.cpp (on the same folder where the images are, with no arguments so it assumes default)
stereo_match left01.jpg right01.jpg -i intrinsics.yml -e extrinsics.yml -p cloud.asc
import cloud.asc on MeshLab
Thanks
The description and the image you've posted do not give a lot of information. But from my experience with 3D reconstruction it seem to be that the extrinsic or intrinsic parameters are not correct.
Thus, something had gone wrong with your calibration. You have to get the intrinsic calibration of both cameras and the extrinsic between your cameras.
The extrinsic parameters represent the relative position and orientation between the cameras. Thus, if they are incorrect anything can happen, mostly the point cloud is extremely degenerated.
The intrinsic parameters describe how the 3D points are projected onto the 2D image. Thus, if you have incorrect intrinsic parameters your point cloud is deformed with a linear transformation (assumed that there is a negligible radial/tangential distortion or distortion of a higher order, respectively).
There is a lot of good literature covering the topic of 3D reconstruction and stereo. One of the most important of them is the book "Multiple View Geometry" from Richard Hartley and Andrew Zisserman.
Related
There are a number of calibration tutorials to calibrate camera images of chessboards in EMGU (OpenCV). They all end up calibrating and then undistorting an image for display. That's cool and all but I need to do machine vision where I am taking an image, identifying the location of a corner or blob or feature in the image and then translating the location of that feature in pixels into real world X, Y coordinates.
Pixel -> mm.
Is this possible with EMGU? If so, how? I'd hate to spend a bunch of time learning EMGU and then not be able to do this crucial function.
Yes, it's certainly possible as the "bread and butter" of OpenCV.
The calibration you are describing, in terms of removing distortions, is a prerequisite to this process. After which, the following applies:
The Intrinsic calibration, or "camera matrix" is the first of two required matrices. The second is the Extrinsic calibration of the camera which is essentially the 6 DoF transform that describes the physical location of the sensor center relative to a coordinate reference frame.
All of the Distortion Coefficients, Intrinsic, and Extrinsic Calibrations are available from a single function in Emgu.CV: CvInvoke.CalibrateCamera This process is best explained, I'm sure, by one of the many tutorials available that you have described.
After that it's as simple as CvInvoke.ProjectPoints to apply the transforms above and produce 3D coordinates from 2D pixel locations.
The key to doing this successfully this providing comprehensive IInputArray objectPoints and IInputArray imagePoints to CvInvoke.CalibrateCamera. Be sure to cause "excitation" by using many images, from many different perspectives.
I am trying to reconstruct the real-world coordinates of 3D points from two images taken from the same camera. The camera is not calibrated, but the movement (translation and rotation) is known. In short:
Requirement:
No calibration
Extra constraints other than image point correspondences:
Known camera translation and rotation
Same camera used in all views
I understand that, from image point correspondences alone, a scene can be reconstructed only up to a projective transformation. With more constraints, an affine or similarity reconstruction may be done. In my case, I need a similarity reconstruction.
Given the above constraints, is a similarity reconstruction possible? If possible, how should I go about doing it?
I have tried to attack the problem from a few angles. Since I am not mathematically fluent, I try to use opencv as much as possible.
findFundamentalMat() from the two images, hopefully extract the two camera matrices somehow, then triangulatePoints(). As you could have guessed, I got stuck in the middle, unable to obtain camera matrices from fundamental matrix.
The textbook "Multiple View Geometry in Computer Vision" (by Hartley and Zisserman) gives an expression (p.256, Result 9.14) that expresses the camera matrices in terms of fundamental matrix and one of the epipoles. However, without knowing the camera's intrinsic parameters (requirement: no calibration), I don't see how I can get the epipole.
I also try to treat my problem as a stereo system and use opencv's stereo*** functions. But they all seem to require human intervention to calibrate, which violates my requirement.
So, that's why I ask the question here today. The key is still, given those extra constraints, is a similarity reconstruction possible? I am not smart enough to understand the wealth of knowledge out there, and not able to come up with my own solution. Any help is appreciated.
I try to match two overlapping images captured with a camera. To do this, I'd like to use OpenCV. I already extracted the features with the SurfFeatureDetector. Now I try to to compute the rotation and translation vector between the two images.
As far as I know, I should use cvFindExtrinsicCameraParams2(). Unfortunately, this method require objectPoints as an argument. These objectPoints are the world coordinates of the extracted features. These are not known in the current context.
Can anybody give me a hint how to solve this problem?
The problem of simultaneously computing relative pose between two images and the unknown 3d world coordinates has been treated here:
Berthold K. P. Horn. Relative orientation revisited. Berthold K. P. Horn. Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 545 Technology ...
EDIT: here is a link to the paper:
http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.4700
Please see my answer to a related question where I propose a solution to this problem:
OpenCV extrinsic camera from feature points
EDIT: You may want to take a look at bundle adjustments too,
http://en.wikipedia.org/wiki/Bundle_adjustment
That assumes an initial estimate is available.
EDIT: I found some code resources you might want to take a look at:
Resource I:
http://www.maths.lth.se/vision/downloads/
Two View Geometry Estimation with Outliers
C++ code for finding the relative orientation of two calibrated
cameras in presence of outliers. The obtained solution is optimal in
the sense that the number of inliers is maximized.
Resource II:
http://lear.inrialpes.fr/people/triggs/src/ Relative orientation from
5 points: a somewhat more polished C routine implementing the minimal
solution for relative orientation of two calibrated cameras from
unknown 3D points. 5 points are required and there can be as many as
10 feasible solutions (but 2-5 is more common). Also requires a few
CLAPACK routines for linear algebra. There's also a short technical
report on this (included with the source).
Resource III:
http://www9.in.tum.de/praktika/ppbv.WS02/doc/html/reference/cpp/toc_tools_stereo.html
vector_to_rel_pose Compute the relative orientation between two
cameras given image point correspondences and known camera parameters
and reconstruct 3D space points.
There is a theoretical solution, however, the OpenCV implementation of camera pose estimation lacks the needed tools.
The theoretical approach:
Step 1: extract the homography (the matrix describing the geometrical transform between images). use findHomography()
Step 2. Decompose the result matrix into rotations and translations. Use cv::solvePnP();
Problem: findHomography() returns a 3x3 matrix, corresponding to a projection from a plane to another. solvePnP() needs a 3x4 matrix, representing the 3D rotation/translation of the objects. I think that with some approximations, you can modify the solvePnP to give you some results, but it requires a lot of math and a very good understanding of 3D geometry.
Read more about at http://en.wikipedia.org/wiki/Transformation_matrix
I am aware of the chessboard camera calibration technique, and have implemented it.
If I have 2 cameras viewing the same scene, and I calibrate both simultaneously with the chessboard technique, can I compute the rotation matrix and translation vector between them? How?
If you have the 3D camera coordinates of the corresponding points, you can compute the optimal rotation matrix and translation vector by Rigid Body Transformation
If You are using OpenCV already then why don't you use cv::stereoCalibrate.
It returns the rotation and translation matrices. The only thing you have to do is to make sure that the calibration chessboard is seen by both of the cameras.
The exact way is shown in .cpp samples provided with OpenCV library( I have 2.2 version and samples were installed by default in /usr/local/share/opencv/samples).
The code example is called stereo_calib.cpp. Although it's not explained clearly what they are doing there (for that You might want to look to "Learning OpenCV"), it's something You can base on.
If I understood you correctly, you have two calibrated cameras observing a common scene, and you wish to recover their spatial arrangement. This is possible (provided you find enough image correspondences) but only up to an unknown factor on translation scale. That is, we can recover rotation (3 degrees of freedom, DOF) and only the direction of the translation (2 DOF). This is because we have no way to tell whether the projected scene is big and the cameras are far, or the scene is small and cameras are near. In the literature, the 5 DOF arrangement is termed relative pose or relative orientation (Google is your friend).
If your measurements are accurate and in general position, 6 point correspondences may be enough for recovering a unique solution. A relatively recent algorithm does exactly that.
Nister, D., "An efficient solution to the five-point relative pose problem," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.26, no.6, pp.756,770, June 2004
doi: 10.1109/TPAMI.2004.17
Update:
Use a structure from motion/bundle adjustment package like Bundler to solve simultaneously for the 3D location of the scene and relative camera parameters.
Any such package requires several inputs:
camera calibrations that you have.
2D pixel locations of points of interest in cameras (use a interest point detection like Harris, DoG (first part of SIFT)).
Correspondences between points of interest from each camera (use a descriptor like SIFT, SURF, SSD, etc. to do the matching).
Note that the solution is up to a certain scale ambiguity. You'll thus need to supply a distance measurement either between the cameras or between a pair of objects in the scene.
Original answer (applies primarily to uncalibrated cameras as the comments kindly point out):
This camera calibration toolbox from Caltech contains the ability to solve and visualize both the intrinsics (lens parameters, etc.) and extrinsics (how the camera positions when each photo is taken). The latter is what you're interested in.
The Hartley and Zisserman blue book is also a great reference. In particular, you may want to look at the chapter on epipolar lines and fundamental matrix which is free online at the link.
i want to find a position of a point with opencv. i calibrated two cameras using cvCalibrateCamera2. so i know both intrinsic and extrinsic parameters. I read that with a known intrinsic and extrinsic parameters, i can reconstruct 3d by triangulation easily. Is there a function in opencv to achive this.I think cvProjectPoint2 may be useful but i don t understand what exactly. So how i can find 3d position of a point.
Thanks.
You first have to find disparities. There are two algorithms implemented in OpenCV - block matching (cvFindStereoCorrespondenceBM) and graph cuts (cvFindStereoCorrespondenceGC). The latter one gives better results but is slower. After disparity detection you can reproject the disparities to 3D using cvReprojectImageTo3D. This gives you distances for each point of the input images that is in both camera views.
Also note that the stereo correspondence algorithms require a rectified image pair (use cvStereoRectify, cvInitUndistortRectifyMap and cvRemap).