I need to implement a spatial data structure to store rectangles then be able to find all rectangles that intersect a given rectangle. This will be implemented in JavaScript.
So far I am developing a Quad Tree to cut down the search space but because it is for a game, all objects that move will need to update its position in the tree. Back to square one.
Are there any data-structures or methods to help? It will need to process around 10,000 objects so brute force isn't good enough.
A hash table works fairly well as an approximate intersection test. Hash tables are used as part of a more sophisticated algorithm for detecting collisions in ODE.
Logically, this test divides the space into a regular grid. Each grid cell is labeled with a list of objects that intersect that cell. The grid is initialized by scanning all objects. I don't know javascript, so I'll use python-ish pseudocode.
for each ob in objects:
for each x in [floor(ob.x_min / grid_size) .. floor(ob.x_max / grid_size)]:
for each y in [floor(ob.y_min / grid_size) .. floor(ob.y_max / grid_size)]:
hashtable[hash(x, y)].append(ob)
To find collisions with a given object, look up near-collisions in the hash table and then apply an exact collision test to each one.
near_collisions = []
for each x in [floor(ob.x_min / grid_size) .. floor(ob.x_max / grid_size)]:
for each y in [floor(ob.y_min / grid_size) .. floor(ob.y_max / grid_size)]:
near_collisions = near_collisions ++ hashtable[hash(x, y)]
remove duplicates from near_collisions
for each ob2 in near_collisions:
if exact_collision_test(ob, ob2):
do_something
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