3D reconstruction from two calibrated cameras - wh

Question

There are many posts about 3D reconstruction from stereo views of known internal calibration, some of which are excellent. I have read a lot of them, and based on what I have read I am trying to compute my own 3D scene reconstruction with the below pipeline / algorithm. I'll set out the method then ask specific questions at the bottom.

0. Calibrate your cameras:

• This means retrieve the camera calibration matrices K₁ and K₂ for Camera 1 and Camera 2. These are 3x3 matrices encapsulating each camera's internal parameters: focal length, principal point offset / image centre. These don't change, you should only need to do this once, well, for each camera as long as you don't zoom or change the resolution you record in.
• Do this offline. Do not argue.
• I'm using OpenCV's CalibrateCamera() and checkerboard routines, but this functionality is also included in the Matlab Camera Calibration toolbox. The OpenCV routines seem to work nicely.

1. Fundamental Matrix F:

• With your cameras now set up as a stereo rig. Determine the fundamental matrix (3x3) of that configuration using point correspondences between the two images/views.
• How you obtain the correspondences is up to you and will depend a lot on the scene itself.
• I am using OpenCV's findFundamentalMat() to get F, which provides a number of options method wise (8-point algorithm, RANSAC, LMEDS).
• You can test the resulting matrix by plugging it into the defining equation of the Fundamental matrix: x'Fx = 0 where x' and x are the raw image point correspondences (x, y) in homogeneous coordinates (x, y, 1) and one of the three-vectors is transposed so that the multiplication makes sense. The nearer to zero for each correspondence, the better F is obeying it's relation. This is equivalent to checking how well the derived F actually maps from one image plane to another. I get an average deflection of ~2px using the 8-point algorithm.

2. Essential Matrix E:

• Compute the Essential matrix directly from F and the calibration matrices.
• E = K₂^TFK₁

3. Internal Constraint upon E:

• E should obey certain constraints. In particular, if decomposed by SVD into USV.t then it's singular values should be = a, a, 0. The first two diagonal elements of S should be equal, and the third zero.
• I was surprised to read here that if this is not true when you test for it, you might choose to fabricate a new Essential matrix from the prior decomposition like so: E_new = U * diag(1,1,0) * V.t which is of course guaranteed to obey the constraint. You have essentially set S = (100,010,000) artificially.

4. Full Camera Projection Matrices:

• There are two camera projection matrices P₁ and P₂. These are 3x4 and obey the x = PX relation. Also, P = K[R|t] and therefore K_inv.P = [R|t] (where the camera calibration has been removed).
• The first matrix P₁ (excluding the calibration matrix K) can be set to [I|0] then P₂ (excluding K) is R|t
• Compute the Rotation and translation between the two cameras R, t from the decomposition of E. There are two possible ways to calculate R (U*W*V.t and U*W.t*V.t) and two ways to calculate t (±third column of U), which means that there are four combinations of Rt, only one of which is valid.
• Compute all four combinations, and choose the one that geometrically corresponds to the situation where a reconstructed point is in front of both cameras. I actually do this by carrying through and calculating the resulting P₂ = [R|t] and triangulating the 3d position of a few correspondences in normalised coordinates to ensure that they have a positive depth (z-coord)

5. Triangulate in 3D

• Finally, combine the recovered 3x4 projection matrices with their respective calibration matrices: P'₁ = K₁P₁ and P'₂ = K₂P₂
• And triangulate the 3-space coordinates of each 2d point correspondence accordingly, for which I am using the LinearLS method from here.

QUESTIONS:

• Are there any howling omissions and/or errors in this method?
• My F matrix is apparently accurate (0.22% deflection in the mapping compared to typical coordinate values), but when testing E against x'Ex = 0 using normalised image correspondences the typical error in that mapping is >100% of the normalised coordinates themselves. Is testing E against xEx = 0 valid, and if so where is that jump in error coming from?
• The error in my fundamental matrix estimation is significantly worse when using RANSAC than the 8pt algorithm, ±50px in the mapping between x and x'. This deeply concerns me.
• 'Enforcing the internal constraint' still sits very weirdly with me - how can it be valid to just manufacture a new Essential matrix from part of the decomposition of the original?
• Is there a more efficient way of determining which combo of R and t to use than calculating P and triangulating some of the normalised coordinates?
• My final re-projection error is hundreds of pixels in 720p images. Am I likely looking at problems in the calibration, determination of P-matrices or the triangulation?

Answer 1

The error in my fundamental matr1ix estimation is significantly worse when using RANSAC than the 8pt algorithm, ±50px in the mapping between x and x'. This deeply concerns me.

Using the 8pt algorithm does not exclude using the RANSAC principle. When using the 8pt algorithm directly which points do you use? You have to choose 8 (good) points by yourself.

In theory you can compute a fundamental matrix from any point correspondences and you often get a degenerated fundamental matrix because the linear equations are not independend. Another point is that the 8pt algorithm uses a overdetermined system of linear equations so that one single outlier will destroy the fundamental matrix.

Have you tried to use the RANSAC result? I bet it represents one of the correct solutions for F.

My F matrix is apparently accurate (0.22% deflection in the mapping compared to typical coordinate values), but when testing E against x'Ex = 0 using normalised image correspondences the typical error in that mapping is >100% of the normalised coordinates themselves. Is testing E against xEx = 0 valid, and if so where is that jump in error coming from?

Again, if F is degenerated, x'Fx = 0 can be for every point correspondence.

Another reason for you incorrect E may be the switch of the cameras (K1T * E * K2 instead of K2T * E * K1). Remember to check: x'Ex = 0

'Enforcing the internal constraint' still sits very weirdly with me - how can it be valid to just manufacture a new Essential matrix from part of the decomposition of the original?

It is explained in 'Multiple View Geometry in Computer Vision' from Hartley and Zisserman. As far as I know it has to do with the minimization of the Frobenius norm of F.

You can Google it and there are pdf resources.

Is there a more efficient way of determining which combo of R and t to use than calculating P and triangulating some of the normalised coordinates?

No as far as I know.

My final re-projection error is hundreds of pixels in 720p images. Am I likely looking at problems in the calibration, determination of P-matrices or the triangulation?

Your rigid body transformation P2 is incorrect because E is incorrect.