Reconstruction improvement

A Christmas of thesis writing is not what I would expect of a great Christmas, but still it was pretty nice! People were happy and having fun throughout the day, and there was only minor fighting (the stepfather can't go a day without sharing his arguments which have no basis in reality). In other words, a nice Christmas even though it was only a single day free from writing and coding...

I have come to terms with a basic failing in the application, which doesn't reconstruct nearly as well as it should. I believe I know two additions which could fix the algorithm, but with less than a week left, it's not realistic to believe in such a solution. The additions would be:

• The addition of a point correspondence correction algorithm (by the original authors dubbed "the optimal solution"). This would correct clicked image points depending on epipolar constraints, which in turn would benefit the triangulation of 3D points.
• Iteration in the algorithm. After reconstructing a set of 3D coordinates, these should be tested by projection back into the image frame - if the results of this re-projection are inaccurate, make a new estimate of the camera pose, which is then used for a new triangulation. After all, the first pose estimation is only done with four manually defined image points, and each point after that is also clicked manually, of course leading to a great deal of error in the reconstruction. Perhaps a similar iteration could be applied to the calculation of the fundamental matrix, the algebraic representation of epipolar geometry.

We decided on a temporary name for the application a few weeks ago. However, the name we chose - Pho2Model - is very similar to another product which has similar features - 'Photomodeler'. This leads to a need for another name if I/we should want to market it. Of course, this would require some corrections in the algorithm, but plans are ready for that, and it could be a nice side project after getting a 'normal job'.

3D Reconstruction from photos and Image noise

For some reason, I had missed a basic step when creating my algorithm for 3D reconstruction from photos. The step, which I have been careful to consider in all similar previous projects, enforces an epipolar constraint and thereby "fixes" the problem of image noise.

The reconstruction done without enforcing this constraint is quite lousy, to say the least. Instead of searching through the software I created for errors (I've created quicker code, but not found any errors after using several methods of searching) I have decided to use 2-3 days for implementing a much more advanced algorithm, which enforces the epipolar constraint, and thereby "fixes" the problem of image noise...

If you're interested, an article named "Triangulation" from Hartley and Sturm, published in 1997 (sorry if any detail is wrong, no ill intention).

Hope this post helps you,
E.Hunefalk [First name not included because of spam risk - with some luck my thesis will published in 2007 though, so the name shouldn't be difficult to find :-)]