and overview video: https://www.youtube.com/watch?v=w6-LK9BOVTA
I think you briefly talked about using heading change and distance for encoding your polyline. So if I understand correctly, something like:
[forwardDistance1, angleDelta1, forwardDistance2, angleDelta2, ...]
Then are you using it kinda like a kd-tree (filter everything within a threshold of forwardDistance1, then angleDelta1, then next dimension and so on)? But then how do you handle the initial scaling? What about cumulative error in heading or distance?
Congrats on the amazing work by the way!
http://depts.washington.edu/madlab/proj/dollar/index.html
which gives a value for how close to polylines are (it normalizes them and does some distance calculations)
the way the vp tree works is you provide a set of data and a metric for distance (in my case, the data was the polylines and the metric was from dollar) and it computes the structure. As long as the metric observes some basic principles (I think it's called triangle inequality) the spatial division will work and you can do a fast search for nearest neighbors.
This was a passion project, they approached me with a set of data -- satellite images, and we discussed different ways of visualizing them, etc.
I'm on Chrome (55.0.2883.95 (64-bit)), though, and I'm getting:
"This experience requires WebGL. Please try again using a WebGL enabled browser, such as Google Chrome"
I actually disabled hardware acceleration because of a macOS Sierra but that would keep logging me out.
Will try messing with the settings.