Collision detection for particle systems

One of the things I’m working on for motor2 is the inclusion of a particle system, mainly for simulating bullets, fluids and soft bodies. The main challenge is to build a fast particle-polygon collision detection which would be able to compute the penetration depth and collision normal for a huge amount of particles. The containment check could be done with a binary search over the vertices of the polygon, an O(n log(n)) operation, but it doesn’t give you the information to push the particle outward.

After reading some papers I finally found the solution to my problem, namely a signed distance field.
A distance field is a scalar field that measures the distance from a given point to an object or data volume. Each element in a distance field specifies its minimum euclidean distance to the shape. Positive and negative distances are used to distinguish outside and inside of the shape (thus signed). This information is precomputed and stored with the shape.

Actually it’s very simple in 2D: First, the shape’s bounding box is divided into a regular grid. Second, each grid cell is analyzed – it can be either outside, inside or intersecting one or multiple edges. For the inside and outside case, a flag is stored with the cell. For the intersecting case, all piercing edges are found and converted into planes, stored in the point-normal form, n (X – P) = 0. At the end you are left with a very simple plane-point distance check.
A lower resolution requires less memory, but makes the lookup slower. The opposite is true for very small cells, because then each cell is likely to contain only one edge to test against. The demo below shows the ‘rasterized’ distance field:

In the next demo you see a ‘pseudo color representation’ of the distance field. As you see, no information is lost, and the original shape can be reconstructed by extracting a point-based contour at true Euclidean distance and any level of accuracy:

The last demo shows the minimum translation distance vector (MDT) obtained by evaluating the distance field. The vector, with its length and direction has the required information to push the particle outward if it’s contained by the polygon:

The steps are:

  1. Transform the particle to the polygon’s local space.
  2. Evaluate the distance field and resolve collision.
  3. Transform the new particle’s position back to world space.

4 dot products, 6 additions (step 1 + 3) and in the best case one array lookup and one dot product (step 2) are required. Of course, the method can fail if a particle moves very fast, in this case the particle’s movement should be modeled as a ray, which is then clipped against the polygon.

The source code for the distance field is not yet included in the motor2 svn, since it would mess up existing classes, but it will be very soon.

Using object pools

Joa Ebert is right when he says that utilizing object pools can make your code perform a lot faster. An object pool is just a container for a bunch of pre-constructed objects that are kept in memory ready for use, rather than being repeatedly allocated and destroyed on demand.
Object pooling makes sense if:

  • you create dozens of short-lived objects in real-time applications like games
  • you need to store and share temporary data throughout complex algorithms
  • the objects are expensive to create (many fields, complex inheritance chain, nested objects)
  • the objects are expensive to remove (unregister listeners, nullify instances)

The only drawback is that memory consumption will raise, but with ridiculously low memory prices this shouldn’t be problem if used wisely ;-)


So here is my manager class which is an attempt to create a lightweight, fast and reusable solution. The implementation is based on a circular list, and the API is very simple. Download: (source, example, asdoc)

I have updated the class so it also accepts a factory for object construction.

First, we create the object pool:

var isDynamic:Boolean = true;
var size:int = 100;

var pool:ObjectPool = new ObjectPool(isDynamic);
pool.allocate(MyClass, size);

The isDynamic flag defines the behavior for an empty pool. If true, the pool automatically creates a new bunch of objects for you. If false, the class throws an Error to indicate that the pool is empty. The size value indicates the pool’s capacity – if the pool is dynamic, the pool grows by the initial size each time it becomes empty so it actually never dries up.

By calling the allocate method the pool is filled with 100 instances of MyClass. You can always reuse the pool for another Class by invoking this method again.

If you need to initialize the objects by passing arguments to it, you can do this with a little helper method called initialize:

pool.initialize("funcName", [arg0, arg1,...]);

This goes through every object and applies the function with the given arguments upon each object. This can also be done by reading each object, calling the function and putting it back:

for (var i:int = 0; i < pool.size; i++)
	var o:MyClass = pool.object;
	o.init(arg0, arg1, ...);
	pool.object = o;

Now to get an instance of MyClass you access the pool like this:

myObjectArray[i] = pool.instance;
//instead of
myObjectArray[i] = new MyClass();

When you are done with your object, instead of throwing it into the garbage collector, you “recycle” it for the next use:

pool.instance = myObjectArray[i];

This assumes that you are storing your instances in an array or something else because if you loose the reference, well it’s lost and can’t be reused anymore :-) And be careful not to assign the object twice, since then your pool would contain duplicates of the same object!

That’s all, pretty simple right ?

Finally, there is the purge() method, which is only interesting for pools that are dynamic. As the pool grows with the demands of the application, it can get quite big. The purge methods scans the pool and removes all allocated but currently unused objects, leaving with a compact representation.


Here is a little demo which demonstrations how the pool works internally. Actually it’s very simple. Pressing the RIGHT arrow key reads an object from the pool (first row), which is then stored in the second row beneath. Pressing the LEFT arrow key gives the object back to the pool. Pressing the ENTER key performs a purge() operation. The purple circle points to the node where the next object is read, the blue circle to an empty node where the insertion is performed.


Benchmarking revealed that it’s always faster to cache instances, even for the generic Object class. All benchmarks were done with the release player 9.0.124 on Vista, an object pool size of 100 and with 100 iterations each to get an average value.

The purple bar indicates the time needed to access the pool:

for (var i:int = 0; i < k; i++) instances[i] = p.instance;

The blue bar measures the task of reading the objects, then putting them back:

for (i = 0; i < k; i++) instances[i] = p.instance;
for (i = 0; i < k; i++) p.instance = instances[i];

The grey bar shows the time needed for creating the objects on the fly:

for (i = 0; i < k; i++) instances[i] = new MyClass();

Here my result:

Caching a native flash Object can be almost 5x faster instead of creating it on the fly.

Almost the same applies to a slightly more complex object like the flash.geom.Point class.

Creating complex objects, here from the flash.display.Sprite class, is extremely slow and up
to 80x faster when pooling.