Quadtree vs Spatial Hashing

Here is a quick visualization of two algorithms used to reduce the the number of collision checks in a 2d plane. The visualization and the quadtree algorithm was originally implemented by Timo Hausmann (https://github.com/timohausmann/quadtree-js/) and slightly adapted by me. I also wrote the spatial hashing implementation.

Background

Lately I’ve been working on a browser platform game using JavaScript and WebGL accelerated canvas powered by PIXI.JS

For the game feeling I’m targeting, it’s important to have a lot of objects at the same time. I noticed that the game slowed down and sometimes frooze completely when I had a too many objects at the same time. The bottle neck was, as I had anticipated, the collision handling. By checking each object for collisions with every other objects, you get a nasty non-linear complexity.

An old problem, of course, but I didn’t quite know what the solution would be, so I started googling. One approach I stumpled upon first was the quadtree data structure (see the wikipedia link for an explanation). I tried a few implementations, but I found the simplest was one by Timo Hausmann. Unfortunately it didn’t seem to reduce the number of collisions checks very much. But why was it slow?

One hack I used to reduce the number of collisions was to limit the number of collided objects per object to 2 or 3. This increased the speed, but made the collision detection inaccurate.

Timo had the good taste to include an excellent visualization of the algorithm in the github repo. His visualization shows a green square (following the mouse pointer) and highlights all the squares that it is collision checked against.

After some googling for alternatives I decided to try spatial hashing. I implemented one spatial hash using the same interface as Timo’s quadtree implementation. I could then save myself a lot of work by retrofitting his visualization for my spatial hashing implementation.

Now I could easily compare both algorithms. To me, was pretty obvious that spatial hashing was more efficient. This was also shown as I plugged in my spatial hash implementation - the performance problem were greatly reduced and I could have 80 objects without the collision detecting became a bottle neck.

It is of course possible, that there are cases where a quadtree is a more efficient data structure than a spatial hash, but for uniform size objects, spatial hashing seems quicker.

The power of visualization

Algorithmic complexity is usually represented with using big O notation. While trying to find a good algorithm for reducing the number of objects to collision check, I realized the power of visualizing algorithms. Using visualizations I gained more insight in the quadtree algorithm than I could have gotten by just reading a formal description of it.

The visualizations

I added run-time control over some parameters using dat.gui. Now you can really compare the algorithms. What I don’t measure is actual cpu time used, but this gives a pretty good hint of how they work.