Pablo, Now that I think about it, there is something odd in the fact that doing a multi-step optimization by changing which variables are optimized works any better than a full optimization. That is, if the current solution is in a local minima with respect to 10 variables, for example, then it will still be in a local minima if fewer than the full number is optimized starting from that point. If there's a way out of trap when optimizing a subset of the variables, then that same way out is available when more variables are allowed to vary. So then, why does manually choosing some subsets of variables in Hugin seem to work better than just letting it optimize everything from the start?
One possibility might be that there are very gently sloped valleys out of the local minima that are below the optimizer tolerance so it stops, whereas when there are less variables being optimized that slope looks relatively larger and above the tolerance. It might make sense to try some experiments on a dataset we know finds a good minima with multi-step optimization but not with one-shot and see if changing the convergence tolerances allows the one-shot to work. Brent On Sep 27, 11:58 am, Pablo d'Angelo <pablo.dang...@web.de> wrote: > Hi Brent, > > Brent schrieb: > > > From a purely empirical point of view, it appears that there is some > > undesirable linkage between these parameters that makes the > > minimization process get trapped into local minima and have difficulty > > finding a solution in some cases. > > I have had the same problem, with both the tilt parameters (TiX,TiY,TiZ, > TiS) and the position parameters (TrX, TrY, TrZ). The problem that the > optimizer gets trapped in local minima isn't really something new, so we > have worked a bit around that with multi-step optimization, and > especially the incremental optimization in hugin and autooptimizer (cmd > line optimisation program, part of the hugin package). I found that with > adding image by image to the optimisation process, I didn't get really > bad random behaviour anymore, even when later "finetuning" the project > and doing reoptimisations, as the project is already close to a nice > minima (I hope....). > > As for correlation between the parameters, I quite sure that d,e and > TrX, TrY are highly correlated when using rectilinear images, especially > if they are shot without large variation in yaw and pitch. > > In my experiements with the graffiti wall example posted by Bruno, I got > some problems when optimizing d,e (linked between all images), even if > the solution before was quite good, and his images contain shots from > many different angles. > > I haven't expected an high correlation between p and TrZ though, so > this is interesting and should be analyzed in more detail. It might just > be that the starting point in the solution space is very far from the > global optima. > > ciao > Pablo --~--~---------~--~----~------------~-------~--~----~ You received this message because you are subscribed to the Google Groups "hugin and other free panoramic software" group. A list of frequently asked questions is available at: http://wiki.panotools.org/Hugin_FAQ To post to this group, send email to hugin-ptx@googlegroups.com To unsubscribe from this group, send email to hugin-ptx-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/hugin-ptx -~----------~----~----~----~------~----~------~--~---
