From: Nick Hurlburt <nickhurlburt@ya...>  20020307 18:06:15

I wrote a bundle adjustment routine to solve an Nview reconstruction problem using vnl_levenberg_marquardt. My vnl_least_squares subclass takes a vector that is the serialization of the projection matrices and returns the vector whose elements are geometric distances dist(PX, x), where X and x are world and image points that are kept unchanged. There are about 20*N image points and N is only 3 or 4 right now. I am not supplying a gradient. The initial guess is reasonably good. Most of the time, the solver responds with something like: vnl_levenberg_marquardt: too many iterations vnl_levenberg_marquardt: 390 iterations, 14403 evaluations, 177 residuals. RMS error start/end 1.8665/0.422231 and then fails. From intermediate measurements I know the solution is converging, albeit slowly and not strictly monotonically, but the solver cuts off and throws away the incomplete but improved results. Can I change the convergence criteria or at least recover the last parameter vector? Also, if you see a way by which I can improve the general algorithm I've described, I'd be interested. On the rare times it does terminate, the error from external reprojection tests isn't always better, sometimes a little worse. __________________________________________________ Do You Yahoo!? Try FREE Yahoo! Mail  the world's greatest free email! http://mail.yahoo.com/ 