@Lex: actually I’ve already read this paper: this is indeed very interesting to me (and IMO much more complex than my proof-of-concept). As explained in this post (and answer) you don’t need to run this algorithm in real-time: this is only needed to bootstrap (or update) a slam algorithm to real world coordinates. So the idea behind my prototype was to run the global pose estimator on the server side (in my case a Corei7 with a GeForce GTX 470). But I’ve also implemented a prototype based on panoramic image which use a reference panoramic image as boostrap (detection based on surf) and a Kalman filter on sensor information (tracking) that will run on an iPad. I’ve implemented the prototype on a low-end device: Samsun UMPC Q1 and my previous company is supposed to port it to the iPad…

Very impressive, great result!
The ISMAR 2011 paper ‘Real-Time Self-Localization from Panoramic Images on Mobile Devices’ by Clemens Arth will interest you as well. He uses a panoramic tracker to locate enough feature points for a 6DOF pose esitimation on a mobile phone (with a small FOV). The idea is to pick up the tracking from there using another system, e.g. with SLAM, PTAM, Optical Flow, etc.

But you have managed to do it without a panorama!

One question: what are the (approximate) specs of the device used to create the movie in this post?

Lex

How would you evaluate the matching performance if we replace FLANN with GPU-based KNN (http://www.i3s.unice.fr/~creative/KNN/) in your implementation?

It show matches in yellow too, has you have done !

@Cesar.Lopez: No, this prototype is still using Bundler (I’ll post a list of alternative I’m working on in another post).