An Octree (3D) uses the same concepts as a Quadtree (2D). If you read and understand the wikipedia article on Quadtrees, you should be able to apply the same concepts into 3D.

Both of these trees allow you to use area-based lookups that can greatly reduce the number of comparisons required to figure out what objects are in a certain area. This can be useful for vis-culling, or even for collisions, depending on your game.

The basic concept is that the world-space is divided up into "buckets": squares for 2D or cubes for 3D. With an empty world, you start with a single square or cube bucket that covers the entire area. As you add objects to the world, you start to subdivide the existing bucket(s) into smaller and smaller buckets, splitting squares into 4 smaller squares (Quadtree), or splitting cubes into 8 smaller cubes (Octree). Each object you add to world is only inserted as deep in the tree as it needs to, and only as deep as it can physically fit completely inside the bucket's bounds. If an object does not fit inside the bounds of the current bucket, you must move the object to the smallest parent bucket that it does completely fit inside. Using trees like this allows you quickly disregard massive chunks of your world with only a few bound checks, instead of comparing against each object in your world.

Note that using either a Quadtree or Octree is overkill if you do not have a lot of objects in your world. There are also open-source solutions to both.

An Octree (3D) uses the same concepts as a Quadtree (2D). If you read and understand the wikipedia article on Quadtrees, you should be able to apply the same concepts into 3D.

Both of these trees allow you to use area-based lookups that can greatly reduce the number of comparisons required to figure out what objects are in a certain area. This can be useful for vis-culling, or even for collisions, depending on your game.

The basic concept is that the world-space is divided up into "buckets": squares for 2D or cubes for 3D. With an empty world, you start with a single square or cube bucket that covers the entire world. As you add objects to the world, you start at the root node and work your way into the tree based on the location and size of the object. If the destination bucket reaches capacity, you subdivide it by splitting squares into 4 smaller squares (Quadtree), or splitting cubes into 8 smaller cubes (Octree). Each object you add to world is only inserted as deep in the tree as it can physically fit completely inside the bucket's bounds. If an object does not fit inside the bounds of the current bucket, you must move the object to the smallest parent bucket that it does completely fit inside. Using trees like this allows you quickly disregard massive chunks of your world with only a few bound checks, instead of comparing against each object in your world.

Note that using either a Quadtree or Octree is overkill if you do not have a lot of objects in your world. There are also open-source solutions to both.

An Octree (3D) uses the same concepts as a Quadtree (2D). If you read and understand the wikipedia article on Quadtrees, you should be able to apply the same concepts into 3D.

Both of these trees allow you to use area-based lookups that can greatly reduce the number of comparisons required to figure out what objects are in a certain area. This can be useful for vis-culling, or even for collisions, depending on your game.

The basic concept is that the world-space is divided up into "buckets": squares for 2D or cubes for 3D. With an empty world, you start with a single square or cube bucket that covers the entire area. As you add objects to the world, you start to subdivide the existing bucket(s) into smaller and smaller buckets, splitting squares into 4 smaller squares (Quadtree), or splitting cubes into 8 smaller cubes (Octree). Each object you add to world is only inserted as deep in the tree as it needs to, and only as deep as it can physically fit completely inside the bucket's bounds. If an object does not fit inside the bounds, it must be stored one layer up. Using trees like this allows you quickly disregard massive chunks of your world with only a few bound checks, instead of comparing against each object in your world.

Note that using either a Quadtree or Octree is overkill if you do not have a lot of objects in your world. There are also open-source solutions to both.

An Octree (3D) uses the same concepts as a Quadtree (2D). If you read and understand the wikipedia article on Quadtrees, you should be able to apply the same concepts into 3D.

Both of these trees allow you to use area-based lookups that can greatly reduce the number of comparisons required to figure out what objects are in a certain area. This can be useful for vis-culling, or even for collisions, depending on your game.

The basic concept is that the world-space is divided up into "buckets": squares for 2D or cubes for 3D. With an empty world, you start with a single square or cube bucket that covers the entire area. As you add objects to the world, you start to subdivide the existing bucket(s) into smaller and smaller buckets, splitting squares into 4 smaller squares (Quadtree), or splitting cubes into 8 smaller cubes (Octree). Each object you add to world is only inserted as deep in the tree as it needs to, and only as deep as it can physically fit completely inside the bucket's bounds. If an object does not fit inside the bounds of the current bucket, you must move the object to the smallest parent bucket that it does completely fit inside. Using trees like this allows you quickly disregard massive chunks of your world with only a few bound checks, instead of comparing against each object in your world.

Note that using either a Quadtree or Octree is overkill if you do not have a lot of objects in your world. There are also open-source solutions to both.