When you say "a key component of Street View", which part is it a key component of? Snapping vehicle track data to existing roads? 3D model computation for scene-to-scene jumps? Both?

If you're snapping track data to roads, how do you deal with areas where you have no existing data? Do you take into account aerial imagery?

</openstreetmap-nerd jealous="true">

Google Street View was filmed at 30fps video format. You can generate a 2D route map directly from it without GPS or when GPS is not available.

I took some notes during the StreetView Q&A on reddit two years ago: http://blog.est.im/archives/554

Unfortunately we can't talk much about this; sorry.

There's some more discussion over at Reddit: http://www.reddit.com/r/programming/comments/t1mo7/ceres_sol...

Thanks for adding your note about integrating this into Blender--that makes the news much more relevant to my interests. :)

I was looking forward to integration of motion tracking into Blender and had a small play with the feature when released. Now I just need to make the time to play some more...

What exactly would you use this in Blender for? As a library for someone to build a script that would turn 2d photos into 3d objects or something? Could you expand on that?

Blender got integrated matchmoving / tracking with the 2.61 release, by way of libmv (which I am the BDFL of). A critical part of many tasks in 3D reconstruction and tracking is a solid, flexible nonlinear minimizer. Since Ceres is so easy to model problems with, is so fast, and is appropriately licensed (unlike some alternatives), it is an enabler for these more sophisticated tracking operations.

Note that Blender's next open movie, http://mango.blender.org, is live-action done with Blender's tracking tools. Ceres is going to be used for that as well.

I guess that one application would be camera tracking to implement automatic compositing of 3d models into videos.

It would be nice if we could use it, or (better) if Google exposed the ability in the Maps API, to fit paths to the most likely road, just like is being done in the demo video on the project page.

The roads are not an input to the optimization shown in the examples. How this works is left as an exercise for the reader.

Since it mentions sensor fusion, is this not just* combining the data from the GPS, compass, accelerometers (and any other sensor input from the car e.g. comparing photos, IR, wifi signals) to smooth out any irregularities in a single sensor and making better guesses where they all agree. If I'm right then the demo video is cheating a bit by only showing the GPS input initially and the rest of the info seems to come from thin air, which might be why people think it's working with the underlying street data. Dead reckoning with a compass and accelerometer and/or wheel odometer/tachograph might also have produced a similarly wonky line, but then you have two different wonky lines to average together to get a better approximation of the true path.

Note this supposition is based almost entirely on doing the first 3 weeks of the Udacity course CS373: Programming a Robotic Car, but it seems to fit quite neatly with the stuff they talk about i.e. a car/robot moving, guessing how far it's moved, taking sensor snapshots of its environment then cleverly combining the data to figure out accurately where it is.

Off-topic: scrubbing back and forth in the blog video (to get a better idea of what was happening) seemed incredibly smooth for me, is this a side effect of Youtube/Firefox using HTML5 video? (edit: trying it out with Flash, I get a fancier, but less useful pop-up when I try to scrub, a tech limitation of Flash or just a design decision?)

* not meaning to underestimate the hard work and genius that underlies this technology, but the same concept is used in such everyday items as Wii remotes and suchlike.

is any geometry point?

I mean, when you have a loop (say, around the manhattan grid), then I can understand how everything works when you have one hard(er) constraint such as, "the two farthest points visited are 2nd street at avenue C and 112th street at 12th avenue" - and get everything else aligned accordingly.

But if no external geometry constraint is involved (roads being the most abundant, but not the only type of external geometry constraint), then this reader has been unsuccessful with the exercise.