Efficient (and well explained) implementation of a Quadtree for 2D collision detection

  1. Efficient Quadtrees

All right, I'll take a shot at this. First a teaser to show the results of what I'll propose involving 20,000 agents (just something I whipped up real quick for this specific question):

enter image description here

The GIF has extremely reduced frame rate and significantly lower res to fit the 2 MB maximum for this site. Here's a video if you want to see the thing at close to full speed: https://streamable.com/3pgmn.

And a GIF with 100k though I had to fiddle with it so much and had to turn off the quadtree lines (didn't seem to want to compress as much with them on) as well as change the way the agents looked to get it to fit in 2 megabytes (I wish making a GIF was as easy as coding a quadtree):

enter image description here

The simulation with 20k agents takes ~3 megabytes of RAM. I can also easily handle 100k smaller agents without sacrificing frame rates, though it leads to a bit of a mess on the screen to the point where you can barely see what's going on as in the GIF above. This is all running in just one thread on my i7 and I'm spending almost half the time according to VTune just drawing this stuff on the screen (just using some basic scalar instructions to plot things one pixel at a time in CPU).

And here's a video with 100,000 agents though it's hard to see what's going on. It's kind of a big video and I couldn't find any decent way to compress it without the whole video turning into a mush (might need to download or cache it first to see it stream at reasonable FPS). With 100k agents the simulation takes around 4.5 megabytes of RAM and the memory use is very stable after running the simulation for about 5 seconds (stops going up or down since it ceases to heap allocate). Same thing in slow motion.

Efficient Quadtree for Collision Detection

All right, so actually quadtrees are not my favorite data structure for this purpose. I tend to prefer a grid hierarchy, like a coarse grid for the world, a finer grid for a region, and an even finer grid for a sub-region (3 fixed levels of dense grids, and no trees involved), with row-based optimizations so that a row that has no entities in it will be deallocated and turned into a null pointer, and likewise completely empty regions or sub-regions turned into nulls. While this simple implementation of the quadtree running in one thread can handle 100k agents on my i7 at 60+ FPS, I've implemented grids that can handle a couple million agents bouncing off each other every frame on older hardware (an i3). Also I always liked how grids made it very easy to predict how much memory they'll require, since they don't subdivide cells. But I'll try to cover how to implement a reasonably efficient quadtree.

Note that I won't go into the full theory of the data structure. I'm assuming that you already know that and are interested in improving performance. I'm also just going into my personal way of tackling this problem which seems to outperform most of the solutions I find online for my cases, but there are many decent ways and these solutions are tailor-fitted to my use cases (very large inputs with everything moving every frame for visual FX in films and television). Other people probably optimize for different use cases. When it comes to spatial indexing structures in particular, I really think the efficiency of the solution tells you more about the implementer than the data structure. Also the same strategies I'll propose to speeding things up also apply in 3 dimensions with octrees.

Node Representation

So first of all, let's cover the node representation:

// Represents a node in the quadtree.
struct QuadNode
    // Points to the first child if this node is a branch or the first
    // element if this node is a leaf.
    int32_t first_child;

    // Stores the number of elements in the leaf or -1 if it this node is
    // not a leaf.
    int32_t count;

It's a total of 8 bytes, and this is very important as it's a key part of the speed. I actually use a smaller one (6 bytes per node) but I'll leave that as an exercise to the reader.

You can probably do without the count. I include that for pathological cases to avoid linearly traversing the elements and counting them each time a leaf node might split. In most common cases a node shouldn't store that many elements. However, I work in visual FX and the pathological cases aren't necessarily rare. You can encounter artists creating content with a boatload of coincident points, massive polygons that span the entire scene, etc, so I end up storing a count.

Where are the AABBs?

So one of the first things people might be wondering is where the bounding boxes (rectangles) are for the nodes. I don't store them. I compute them on the fly. I'm kinda surprised most people don't do that in the code I've seen. For me, they're only stored with the tree structure (basically just one AABB for the root).

That might seem like it'd be more expensive to be computing these on the fly, but reducing the memory use of a node can proportionally reduce cache misses when you traverse the tree, and those cache miss reductions tend to be more significant than having to do a couple of bitshifts and some additions/subtractions during traversal. Traversal looks like this:

static QuadNodeList find_leaves(const Quadtree& tree, const QuadNodeData& root, const int rect[4])
    QuadNodeList leaves, to_process;
    while (to_process.size() > 0)
        const QuadNodeData nd = to_process.pop_back();

        // If this node is a leaf, insert it to the list.
        if (tree.nodes[nd.index].count != -1)
            // Otherwise push the children that intersect the rectangle.
            const int mx = nd.crect[0], my = nd.crect[1];
            const int hx = nd.crect[2] >> 1, hy = nd.crect[3] >> 1;
            const int fc = tree.nodes[nd.index].first_child;
            const int l = mx-hx, t = my-hx, r = mx+hx, b = my+hy;

            if (rect[1] <= my)
                if (rect[0] <= mx)
                    to_process.push_back(child_data(l,t, hx, hy, fc+0, nd.depth+1));
                if (rect[2] > mx)
                    to_process.push_back(child_data(r,t, hx, hy, fc+1, nd.depth+1));
            if (rect[3] > my)
                if (rect[0] <= mx)
                    to_process.push_back(child_data(l,b, hx, hy, fc+2, nd.depth+1));
                if (rect[2] > mx)
                    to_process.push_back(child_data(r,b, hx, hy, fc+3, nd.depth+1));
    return leaves;

Omitting the AABBs is one of the most unusual things I do (I keep looking for other people doing it just to find a peer and fail), but I've measured the before and after and it did reduce times considerably, at least on very large inputs, to compact the quadtree node substantially and just compute AABBs on the fly during traversal. Space and time aren't always diametrically opposed. Sometimes reducing space also means reducing time given how much performance is dominated by the memory hierarchy these days. I've even sped up some real world operations applied on massive inputs by compressing the data to quarter the memory use and decompressing on the fly.

I don't know why many people choose to cache the AABBs: whether it's programming convenience or if it's genuinely faster in their cases. Yet for data structures which split evenly down the center like regular quadtrees and octrees, I'd suggest measuring the impact of omitting the AABBs and computing them on the fly. You might be quite surprised. Of course it makes sense to store AABBs for structures that don't split evenly like Kd-trees and BVHs as well as loose quadtrees.


I don't use floating-point for spatial indexes and maybe that's why I see improved performance just computing AABBs on the fly with right shifts for division by 2 and so forth. That said, at least SPFP seems really fast nowadays. I don't know since I haven't measured the difference. I just use integers by preference even though I'm generally working with floating-point inputs (mesh vertices, particles, etc). I just end up converting them to integer coordinates for the purpose of partitioning and performing spatial queries. I'm not sure if there's any major speed benefit of doing this anymore. It's just a habit and preference since I find it easier to reason about integers without having to think about denormalized FP and all that.

Centered AABBs

While I only store a bounding box for the root, it helps to use a representation that stores a center and half size for nodes while using a left/top/right/bottom representation for queries to minimize the amount of arithmetic involved.

Contiguous Children

This is likewise key, and if we refer back to the node rep:

struct QuadNode
    int32_t first_child;

We don't need to store an array of children because all 4 children are contiguous:

first_child+0 = index to 1st child (TL)
first_child+1 = index to 2nd child (TR)
first_child+2 = index to 3nd child (BL)
first_child+3 = index to 4th child (BR)

This not only significantly reduces cache misses on traversal but also allows us to significantly shrink our nodes which further reduces cache misses, storing only one 32-bit index (4 bytes) instead of an array of 4 (16 bytes).

This does mean that if you need to transfer elements to just a couple of quadrants of a parent when it splits, it must still allocate all 4 child leaves to store elements in just two quadrants while having two empty leaves as children. However, the trade-off is more than worth it performance-wise at least in my use cases, and remember that a node only takes 8 bytes given how much we've compacted it.

When deallocating children, we deallocate all four at a time. I do this in constant-time using an indexed free list, like so:

enter image description here

Except we're pooling out memory chunks containing 4 contiguous elements instead of one at a time. This makes it so we usually don't need to involve any heap allocations or deallocations during the simulation. A group of 4 nodes gets marked as freed indivisibly only to then be reclaimed indivisibly in a subsequent split of another leaf node.

Deferred Cleanup

I don't update the quadtree's structure right away upon removing elements. When I remove an element, I just descend down the tree to the child node(s) it occupies and then remove the element, but I don't bother to do anything more just yet even if the leaves become empty.

Instead I do a deferred cleanup like this:

void Quadtree::cleanup()
    // Only process the root if it's not a leaf.
    SmallList<int> to_process;
    if (nodes[0].count == -1)

    while (to_process.size() > 0)
        const int node_index = to_process.pop_back();
        QuadNode& node = nodes[node_index];

        // Loop through the children.
        int num_empty_leaves = 0;
        for (int j=0; j < 4; ++j)
            const int child_index = node.first_child + j;
            const QuadNode& child = nodes[child_index];

            // Increment empty leaf count if the child is an empty 
            // leaf. Otherwise if the child is a branch, add it to
            // the stack to be processed in the next iteration.
            if (child.count == 0)
            else if (child.count == -1)

        // If all the children were empty leaves, remove them and 
        // make this node the new empty leaf.
        if (num_empty_leaves == 4)
            // Push all 4 children to the free list.
            nodes[node.first_child].first_child = free_node;
            free_node = node.first_child;

            // Make this node the new empty leaf.
            node.first_child = -1;
            node.count = 0;

This is called at the end of every single frame after moving all the agents. The reason I do this kind of deferred removal of empty leaf nodes in multiple iterations and not all at once in the process of removing a single element is that element A might move to node N2, making N1 empty. However, element B might, in the same frame, move to N1, making it occupied again.

With the deferred cleanup, we can handle such cases without unnecessarily removing children only to add them right back when another element moves into that quadrant.

Moving elements in my case is a straightforward: 1) remove element, 2) move it, 3) reinsert it to the quadtree. After we move all the elements and at the end of a frame (not time step, there could be multiple time steps per frame), the cleanup function above is called to remove the children from a parent which has 4 empty leaves as children, which effectively turns that parent into the new empty leaf which might then be cleaned up in the next frame with a subsequent cleanup call (or not if things get inserted to it or if the empty leaf's siblings are non-empty).

Let's look at the deferred cleanup visually:

enter image description here

Starting with this, let's say we remove some elements from the tree leaving us with 4 empty leaves:

enter image description here

At this point, if we call cleanup, it will remove 4 leaves if it finds 4 empty child leaves and turn the parent into an empty leaf, like so:

enter image description here

Let's say we remove some more elements:

enter image description here

... and then call cleanup again:

enter image description here

If we call it yet again, we end up with this:

enter image description here

... at which point the root itself turns into an empty leaf. However, the cleanup method never removes the root (it only removes children). Again the main point of doing it deferred this way and in multiple steps is to reduce the amount of potential redundant work that could occur per time step (which can be a lot) if we did this all immediately every single time an element is removed from the tree. It also helps to distribute that works across frames to avoid stutters.

TBH, I originally applied this "deferred cleanup" technique in a DOS game I wrote in C out of sheer laziness! I didn't want to bother with descending down the tree, removing elements, and then removing nodes in a bottom-up fashion back then because I originally wrote the tree to favor top-down traversal (not top-down and back up again) and really thought this lazy solution was a productivity compromise (sacrificing optimal performance to get implemented faster). However, many years later, I actually got around to implementing quadtree removal in ways that immediately started removing nodes and, to my surprise, I actually significantly made it slower with more unpredictable and stuttery frame rates. The deferred cleanup, in spite of originally being inspired by my programmer laziness, was actually (and accidentally) a very effective optimization for dynamic scenes.

Singly-Linked Index Lists for Elements

For elements, I use this representation:

// Represents an element in the quadtree.
struct QuadElt
    // Stores the ID for the element (can be used to
    // refer to external data).
    int id;

    // Stores the rectangle for the element.
    int x1, y1, x2, y2;

// Represents an element node in the quadtree.
struct QuadEltNode
    // Points to the next element in the leaf node. A value of -1 
    // indicates the end of the list.
    int next;

    // Stores the element index.
    int element;

I use an "element node" which is separate from "element". An element is only inserted once to the quadtree no matter how many cells it occupies. However, for each cell it occupies, an "element node" is inserted which indexes that element.

The element node is a singly-linked index list node into an array, and also using the free list method above. This incurs some more cache misses over storing all the elements contiguously for a leaf. However, given that this quadtree is for very dynamic data which is moving and colliding every single time step, it generally takes more processing time than it saves to seek out a perfectly contiguous representation for the leaf elements (you would effectively have to implement a variable-sized memory allocator which is really fast, and that's far from an easy thing to do). So I use the singly-linked index list which allows a free list constant-time approach to allocation/deallocation. When you use that representation, you can transfer elements from split parents to new leaves by just changing a few integers.


Oh, I should mention this. Naturally it helps if you don't heap allocate just to store a temporary stack of nodes for non-recursive traversal. SmallList<T> is similar to vector<T> except it won't involve a heap allocation until you insert more than 128 elements to it. It's similar to SBO string optimizations in the C++ standard lib. It's something I implemented and have been using for ages and it does help a lot to make sure you use the stack whenever possible.

Tree Representation

Here's the representation of the quadtree itself:

struct Quadtree
    // Stores all the elements in the quadtree.
    FreeList<QuadElt> elts;

    // Stores all the element nodes in the quadtree.
    FreeList<QuadEltNode> elt_nodes;

    // Stores all the nodes in the quadtree. The first node in this
    // sequence is always the root.
    std::vector<QuadNode> nodes;

    // Stores the quadtree extents.
    QuadCRect root_rect;

    // Stores the first free node in the quadtree to be reclaimed as 4
    // contiguous nodes at once. A value of -1 indicates that the free
    // list is empty, at which point we simply insert 4 nodes to the
    // back of the nodes array.
    int free_node;

    // Stores the maximum depth allowed for the quadtree.
    int max_depth;

As pointed out above, we store a single rectangle for the root (root_rect). All sub-rects are computed on the fly. All nodes are stored in contiguously in an array (std::vector<QuadNode>) along with the elements and element nodes (in FreeList<T>).


I use a FreeList data structure which is basically an array (and random-access sequence) that lets you remove elements from anywhere in constant-time (leaving holes behind which get reclaimed upon subsequent insertions in constant-time). Here's a simplified version which doesn't bother with handling non-trivial data types (doesn't use placement new or manual destruction calls):

/// Provides an indexed free list with constant-time removals from anywhere
/// in the list without invalidating indices. T must be trivially constructible 
/// and destructible.
template <class T>
class FreeList
    /// Creates a new free list.

    /// Inserts an element to the free list and returns an index to it.
    int insert(const T& element);

    // Removes the nth element from the free list.
    void erase(int n);

    // Removes all elements from the free list.
    void clear();

    // Returns the range of valid indices.
    int range() const;

    // Returns the nth element.
    T& operator[](int n);

    // Returns the nth element.
    const T& operator[](int n) const;

    union FreeElement
        T element;
        int next;
    std::vector<FreeElement> data;
    int first_free;

template <class T>
FreeList<T>::FreeList(): first_free(-1)

template <class T>
int FreeList<T>::insert(const T& element)
    if (first_free != -1)
        const int index = first_free;
        first_free = data[first_free].next;
        data[index].element = element;
        return index;
        FreeElement fe;
        fe.element = element;
        return static_cast<int>(data.size() - 1);

template <class T>
void FreeList<T>::erase(int n)
    data[n].next = first_free;
    first_free = n;

template <class T>
void FreeList<T>::clear()
    first_free = -1;

template <class T>
int FreeList<T>::range() const
    return static_cast<int>(data.size());

template <class T>
T& FreeList<T>::operator[](int n)
    return data[n].element;

template <class T>
const T& FreeList<T>::operator[](int n) const
    return data[n].element;

I have one which does work with non-trivial types and provides iterators and so forth but is much more involved. These days I tend to work more with trivially constructible/destructible C-style structs anyway (only using non-trivial user-defined types for high-level stuff).

Maximum Tree Depth

I prevent the tree from subdividing too much by specifying a max depth allowed. For the quick simulation I whipped up, I used 8. For me this is crucial since again, in VFX I encounter pathological cases a lot including content created by artists with lots of coincident or overlapping elements which, without a maximum tree depth limit, could want it to subdivide indefinitely.

There is a bit of fine-tuning if you want optimal performance with respect to max depth allowed and how many elements you allow to be stored in a leaf before it splits into 4 children. I tend to find the optimal results are gained with something around 8 elements max per node before it splits, and a max depth set so that the smallest cell size is a little over the size of your average agent (otherwise more single agents could be inserted into multiple leaves).

Collision and Queries

There are a couple of ways to do the collision detection and queries. I often see people do it like this:

for each element in scene:
     use quad tree to check for collision against other elements

This is very straightforward but the problem with this approach is that the first element in the scene might be in a totally different location in the world from the second. As a result, the paths we take down the quadtree could be totally sporadic. We might traverse one path to a leaf and then want to go down that same path again for the first element as, say, the 50,000th element. By this time the nodes involved in that path may have already been evicted from the CPU cache. So I recommend doing it this way:

traversed = {}
gather quadtree leaves
for each leaf in leaves:
     for each element in leaf:
          if not traversed[element]:
              use quad tree to check for collision against other elements
              traversed[element] = true                  

While that's quite a bit more code and requires we keep a traversed bitset or parallel array of some sort to avoid processing elements twice (since they may be inserted in more than one leaf), it helps make sure that we descend the same paths down the quadtree throughout the loop. That helps keep things much more cache-friendly. Also if after attempting to move the element in the time step, it's still encompassed entirely in that leaf node, we don't even need to work our way back up again from the root (we can just check that one leaf only).

Common Inefficiencies: Things to Avoid

While there are many ways to skin the cat and achieve an efficient solution, there is a common way to achieve a very inefficient solution. And I've encountered my share of very inefficient quadtrees, kd trees, and octrees in my career working in VFX. We're talking over a gigabyte of memory use just to partition a mesh with 100k triangles while taking 30 secs to build, when a decent implementation should be able to do the same hundreds of times a second and just take a few megs. There are many people whipping these up to solve problems who are theoretical wizards but didn't pay much attention to memory efficiency.

So the absolute most common no-no I see is to store one or more full-blown containers with each tree node. By full-blown container, I mean something that owns and allocates and frees its own memory, like this:

struct Node

     // Stores the elements in the node.
     List<Element> elements;

And List<Element> might be a list in Python, an ArrayList in Java or C#, std::vector in C++, etc: some data structure that manages its own memory/resources.

The problem here is that while such containers are very efficiently implemented for storing a large number of elements, all of them in all languages are extremely inefficient if you instantiate a boatload of them only to store a few elements in each one. One of the reasons is that the container metadata tends to be quite explosive in memory usage at such a granular level of a single tree node. A container might need to store a size, capacity, a pointer/reference to data it allocates, etc, and all for a generalized purpose so it might use 64-bit integers for size and capacity. As a result the metadata just for an empty container might be 24 bytes which is already 3 times larger than the entirety of the node representation I proposed, and that's just for an empty container designed to store elements in leaves.

Furthermore each container often wants to either heap/GC-allocate on insertion or require even more preallocated memory in advance (at which point it might take 64 bytes just for the container itself). So that either becomes slow because of all the allocations (note that GC allocations are really fast initially in some implementations like JVM, but that's only for the initial burst Eden cycle) or because we're incurring a boatload of cache misses because the nodes are so huge that barely any fit into the lower levels of the CPU cache on traversal, or both.

Yet this is a very natural inclination and makes intuitive sense since we talk about these structures theoretically using language like, "Elements are stored in the leaves" which suggests storing a container of elements in leaf nodes. Unfortunately it has an explosive cost in terms of memory use and processing. So avoid this if the desire is to create something reasonably efficient. Make the Node share and point to (refer to) or index memory allocated and stored for the entire tree, not for every single individual node. In actuality the elements shouldn't be stored in the leaves.

Elements should be stored in the tree and leaf nodes should index or point to those elements.


Phew, so those are the main things I do to achieve what is hopefully considered a decent-performing solution. I hope that helps. Note that I am aiming this at a somewhat advanced level for people who have already implemented quadtrees at least once or twice. If you have any questions, feel free to shoot.

Since this question is a bit broad, I might come and edit it and keep tweaking and expanding it over time if it doesn't get closed (I love these types of questions since they give us an excuse to write about our experiences working in the field, but the site doesn't always like them). I'm also hoping some experts might jump in with alternative solutions I can learn from and perhaps use to improve mine further.

Again quadtrees aren't actually my favorite data structure for extremely dynamic collision scenarios like this. So I probably have a thing or two to learn from people who do favor quadtrees for this purpose and have been tweaking and tuning them for years. Mostly I use quadtrees for static data that doesn't move around every frame, and for those I use a very different representation from the one proposed above.

2. Fundamentals

For this answer (sorry, I ran out of the character limit again), I'll focus more on fundamentals aimed at someone new to these structures.

All right, so let's say we have a bunch of elements like this in space:

enter image description here

And we want to find out what element is under the mouse cursor, or what elements intersect/collide with each other, or what the nearest element is to another element, or something of this sort.

In that case, if the only data we had was a bunch of element positions and sizes/radii in space, we'd have to loop through everything to find out what element is within a given search area. For collision detection, we'd have to loop through every single element and then, for each element, loop through all other elements, making it an explosive quadratic complexity algorithm. That isn't going to hold up on non-trivial input sizes.


So what can we do about this problem? One of the straightforward approaches is to subdivide the search space (screen, e.g.) into a fixed number of cells, like so:

enter image description here

Now let's say you want to find the element under your mouse cursor at position (cx, cy). In that case, all we have to do is check the elements in the cell under mouse cursor:

grid_x = floor(cx / cell_size);
grid_y = floor(cy / cell_size);
for each element in cell(grid_x, grid_y):
     if element is under cx,cy:
         do something with element (hover highlight it, e.g)

Similar thing for collision detection. If we want to see what elements intersect (collide) with a given element:

grid_x1 = floor(element.x1 / cell_size);
grid_y1 = floor(element.y1 / cell_size);
grid_x2 = floor(element.x2 / cell_size);
grid_y2 = floor(element.y2 / cell_size);

for grid_y = grid_y1, grid_y2:
     for grid_x = grid_x1, grid_x2:
         for each other_element in cell(grid_x, grid_y):
             if element != other_element and collide(element, other_element):
                 // The two elements intersect. Do something in response
                 // to the collision.

And I recommend people start this way by dividing up the space/screen into a fixed number of grid cells like 10x10, or 100x100, or even 1000x1000. Some people might think 1000x1000 would be explosive in memory use but you can make each cell require only 4 bytes with 32-bit integers, like so:

enter image description here

... at which point even a million cells takes less than 4 megabytes.

Downside of a Fixed-Resolution Grid

The fixed resolution grid is a fantastic data structure for this problem if you ask me (my personal favorite for collision detection), but it does have some weaknesses.

Imagine you have a Lord of the Rings video game. Let's say many of your units are small units on the map like humans, orcs, and elves. However, you also have some gigantic units like dragons and ents.

Here a problem with the fixed resolution of the grid is that while your cell sizes might be optimal for storing those small units like humans and elves and orcs which occupy just 1 cell most of the time, the huge dudes like dragons and ents might want to occupy many cells, say, 400 cells (20x20). As a result we have to insert those big guys into many cells and store a lot of redundant data.

Also let's say you want to search a large rectangular region of the map for units of interest. In that case, you might have to check way more cells than theoretically optimal.

This is the main disadvantage of a fixed resolution grid*. We end up potentially having to insert large things and store them into far more cells than we should ideally have to store, and for large search areas, we may have to check far more cells than we should ideally have to search.

  • That said, putting aside the theory, often you can work with grids in a way that is very cache-friendly in ways similar to image processing. As a result, while it has these theoretical disadvantages, in practice the simplicity and ease of implementing cache-friendly traversal patterns can make the grid a lot better than it sounds.


So quadtrees are one solution to this problem. Instead of using a fixed resolution grid, so to speak, they adapt the resolution based on some criteria, while subdividing/splitting into 4 child cells to increase resolution. For example, we might say a cell should split if there are more than 2 children in a given cell. In that case, this:

enter image description here

Ends up becoming this:

enter image description here

And now we have a pretty nice representation where no cell stores more than 2 elements. Meanwhile let's consider what happens if we insert a huge dragon:

enter image description here

Here, unlike the fixed resolution grid, the dragon can just be inserted into one cell since the cell it occupies only has one element in it. Likewise if we search a big area of the map, we won't have to check so many cells unless there are many elements occupying the cells.


So how do we implement one of these thingies? Well, it's a tree at the end of the day, and a 4-ary tree at that. So we start off with the notion of a root node with 4 children, but they're currently null/nil and the root is also a leaf at the moment:

enter image description here


Let's start inserting some elements, and again for simplicity, let's say a node should split when it has more than 2 elements. So we'll insert an element, and when we insert an element we should insert it to the leaves (cells) in which it belongs. In this case we only have one, the root node/cell, and so we'll insert it there:

enter image description here

... and let's insert another:

enter image description here

... and yet another:

enter image description here

And now we have more than 2 elements in a leaf node. It should now split. At this point we create 4 children to the leaf node (our root in this case) and then transfer the elements from the leaf being split (the root) into the appropriate quadrants based on the area/cell each element occupies in space:

enter image description here

Let's insert another element, and again to the appropriate leaf in which it belongs:

enter image description here

... and another:

enter image description here

And now we have more than 2 elements in a leaf again, so we should split it into 4 quadrants (children):

enter image description here

And that's the basic idea. One of the things you might notice is that when we're inserting elements that aren't infinitesimally small points, they can easily overlap multiple cells/nodes.

As a result if we have many elements that overlap many boundaries between cells, they could end up wanting to subdivide a whole lot, possibly infinitely. To mitigate this issue, some people choose to split the element. If all you associate with an element is a rectangle, it's fairly straightforward to dice up rectangles. Other people might just put a depth/recursion limit on how much the tree can split. I tend to prefer the latter solution for collision detection scenarios between these two since I find it at least easier to implement more efficiently. However, there's another alternative: loose representations, and that'll be covered in a different section.

Also if you have elements right on top of each other, then your tree could want to split indefinitely even if you're storing infinitesimally small points. For example, if you have 25 points right on top of each other in space (a scenario I encounter rather often in VFX), then your tree will want to split indefinitely without a recursion/depth limit no matter what. As a result, to handle pathological cases, you might need a depth limit even if you do dice up elements.

Removing Elements

Removing elements is covered in the first answer along with removing nodes to clean up the tree and remove empty leaves. But basically all we do to remove an element using my proposed approach is just descend down the tree to where the leaf/leaves in which the element is stored (which you can determine using its rectangle, e.g.), and remove it from those leaves.

Then to start removing empty leaf nodes, we use a deferred cleanup approach covered in the original answer.


I'm running short on time but will try to come back to this one and keep improving the answer. If people want an exercise, I would suggest implementing a plain old fixed-resolution grid, and see if you can get it down to where each cell is just a 32-bit integer. First understand the grid and its inherent problems before considering the quadtree, and you may be just fine with the grid. It might even provide you the most optimal solution depending on how efficiently you can implement a grid vs. a quadtree.

Edit: Fine/Coarse Grids and Row Optimizations

I got lots of questions on this so I'll go briefly into it. It's actually incredibly dumb-simple and might disappoint people who thought it was some fancy thing. So let's start with a simple fixed-resolution grid representation. I'll use pointers here to make it as simple as possible (although I recommend using an array and indices later on to have more control over memory use and access patterns).

// Stores a node in a grid cell.
struct Node
    // Stores a pointer to the next node or null if we're at the end of
    // the list.
    Node* next = nullptr;

    // Stores a pointer to the element in the cell.
    Element* element = nullptr;

// Stores a 1000x1000 grid. Hard-coded for simple illustration.
Node* grid[1000][1000] = {};

As covered in the other answers, fixed-resolution grids are actually much more decent than they look even if they seem so dumb compared to tree-based solutions with variable resolution. However, they do come with a drawback that if we want to search a large parameter (like a huge circular or rectangular area), they have to loop through many grid cells. So we can mitigate that cost by storing a coarser grid:

// Stores a lower-resolution 500x500 parent grid (can use bitsets instead
// of bools). Stores true if one or more elements occupies the cell or
// false if the cell is empty.
bool parent_grid[500][500] = {};

// Stores an even lower-resolution 100x100 grid. It functions similarly
// as the parent grid, storing true if ane element occupies the cell or
// false otherwise.
bool grandparent_grid[100][100] = {};

We can keep going and you can adjust the resolutions and the number of grids you use as you see fit. By doing this, when we want to search a large parameter, we start off checking the grandparent grid before we search the parent grid, and the parent before we check the full-resolution grid. We only proceed if the cells are not completely empty. This can help us exclude a whole bunch of cells to check from the highest-resolution grid in lots of cases involving large search parameters.

That's really all there is to it. It does require storing all the highest-resolution cells in memory unlike a quadtree, but I've always found it much easier to optimize since we don't have to chase pointers/indices around to traverse the children of each tree node. Instead, we just do array lookups with access patterns that are very cache-friendly.

Row-Based Optimizations

So row-based optimization is also dumb simple (although it only applies when we use arrays and indices instead of pointers to nodes*).

  • Or custom memory allocators, but I really don't recommend using them for most purposes as it's quite unwieldy to have to deal with allocator and data structure separately. It is so much simpler for linked structures to emulate the effect of bulk allocation/deallocation and contiguous access patterns by just storing/reallocating arrays (ex: std::vector) and indices into them. For linked structures in particular and given that we now have 64-bit addressing, it's especially helpful to cut down the size of links to 32-bits or less by turning them into indices into a particular array unless you actually need to store more than 2^32-1 elements in your data structure.

If we imagine a grid like this:

enter image description here

... because we are storing all the nodes in one list structure for the entire grid, the memory stride to get from one element in a cell to the next could be quite large, causing us to skip around a lot in memory and incurring more cache misses as a result (and also loading more irrelevant data into cache lines).

We could eliminate that completely by storing a whole separate node list for every single cell (in which case all the elements in a cell could be stored perfectly contiguously), but that could be quite explosive in memory use and very inefficient in other ways. So the balance is to just store a separate node list per row. We improve spatial locality that way without having an explosive number of separate lists since there aren't that many rows compared to the total number of cells (rows * columns). Another thing you can do when you do that is that when a row is completely empty, you can deallocate even the memory for the grid cells for that entire row and just turn the entire row into a null pointer.

Finally, this enables more opportunities to parallelize insertion/removal/access, since you can guarantee that it's safe provided no two threads are modifying/accessing the same row at one time (something that is often quite easy to ensure).

5. Loose/Tight Double-Grid With 500k Agents

enter image description here

Above is a little GIF showing 500,000 agents bouncing off of each other every time step using a new "loose/tight grid" data structure I was inspired to create after writing the answer about loose quadtrees. I'll try to go over how it works.

It is the best-performing data structure so far among all the ones I've shown that I've implemented (though it could just be me), handling half a million agents better than the initial quadtree handled 100k, and better than the loose quadtree handled 250k. It also requires the least amount of memory and has the most stable memory use among these three. This is all still working in just one thread, no SIMD code, no fancy micro-optimizations as I typically apply for production code -- just a straightforward implementation from a couple hours of work.

I also improved the drawing bottlenecks without improving my rasterization code whatsoever. It's because the grid lets me easily traverse it in a way that's cache-friendly for image processing (drawing the elements in the cells of the grid one-by-one coincidentally happens to lead to very cache-friendly image processing patterns when rasterizing).

Funnily enough it also took the shortest time for me to implement (only 2 hours while I spent 5 or 6 hours on the loose quadtree), and it also requires the least amount of code (and arguably has the simplest code). Though that might just be because I've accumulated so much experience implementing grids.

Loose/Tight Double Grid

So I covered how grids worked in the fundamentals section (see part 2), but this is a "loose grid". Each grid cell stores its own bounding box that is allowed to shrink as elements are removed and grow as elements are added. As a result each element only needs to be inserted once into the grid based on which cell its center position lands inside, like so:

// Ideally use multiplication here with inv_cell_w or inv_cell_h.
int cell_x = clamp(floor(elt_x / cell_w), 0, num_cols-1);
int cell_y = clamp(floor(ely_y / cell_h), 0, num_rows-1);
int cell_idx = cell_y*num_rows + cell_x;
// Insert element to cell at 'cell_idx' and expand the loose cell's AABB.

Cells store elements and AABBs like this:

struct LGridLooseCell
    // Stores the index to the first element using an indexed SLL.
    int head;

    // Stores the extents of the grid cell relative to the upper-left corner
    // of the grid which expands and shrinks with the elements inserted and 
    // removed.
    float l, t, r, b;

However, loose cells pose a conceptual problem. Given that they have these variable-sized bounding boxes which can grow huge if we insert a huge element, how do we avoid checking every single freaking cell of the grid when we want to find out which loose cells and corresponding elements intersect a search rectangle? There could be a case where we're searching the upper-right corner of the loose grid but there's a cell in the lower-left on the opposite side which has grown large enough to intersect that area as well. Without a solution to this problem, we're reduced to having to check all the loose cells in linear time for matches.

... and the solution is that actually this is a "double grid". The loose grid cells themselves are partitioned into a tight grid. When we do the analogical search above, we first look through the tight grid like so:

tx1 = clamp(floor(search_x1 / cell_w), 0, num_cols-1);
tx2 = clamp(floor(search_x2 / cell_w), 0, num_cols-1);
ty1 = clamp(floor(search_y1 / cell_h), 0, num_rows-1);
ty2 = clamp(floor(search_y2 / cell_h), 0, num_rows-1);

for ty = ty1, ty2:
    trow = ty * num_cols
    for tx = tx1, tx2:
        tight_cell = tight_cells[trow + tx];
        for each loose_cell in tight_cell:
            if loose_cell intersects search area:
                for each element in loose_cell:
                    if element intersects search area:
                        add element to query results

Tight cells store a singly-linked index list of loose cells, like so:

struct LGridLooseCellNode
    // Points to the next loose cell node in the tight cell.
    int next;

    // Stores an index to the loose cell.
    int cell_idx;

struct LGridTightCell
    // Stores the index to the first loose cell node in the tight cell using 
    // an indexed SLL.
    int head;

And voila, that's the basic idea of the "loose double-grid". When we insert an element, we expand the loose cell's AABB just as we do for a loose quadtree, only in constant-time instead of logarithmic time. However, we also insert the loose cell to the tight grid in constant time based on its rectangle (and it could be inserted into multiple cells).

The combo of these two (tight grid to rapidly find loose cells, and loose cells to rapidly find elements) gives a very lovely data structure where each element is inserted into a single cell with constant time searches, insertion, and removals.

The only big downside I see is that we do have to store all these cells and potentially still have to search more cells than we need, but they're still reasonably cheap (20 bytes per cell in my case) and it's easy to traverse the cells on searches in a very cache-friendly access pattern.

I recommend giving this idea of "loose grids" a try. It is arguably a lot easier to implement than quadtrees and loose quadtrees and, more importantly, optimize, since it immediately lends itself to a cache-friendly memory layout. As a super cool bonus, if you can anticipate the number of agents in your world in advance, it is almost 100% perfectly stable and immediately in terms of memory use, since an element always occupies exactly one cell, and the number of cells total is fixed (since they don't subdivide/split). The only minor instability in memory use is that those loose cells might expand and get inserted into additional tight cells in the coarser grid every now and then, yet this should be quite infrequent. As a result, the memory use is very stable/consistent/predictable and also often the corresponding frame rates. That could be a huge bonus for certain hardware and software where you want to pre-allocate all memory in advance.

It's also really easy to make it work with SIMD to do multiple coherent queries simultaneously with vectorized code (in addition to multithreading), since the traversal, if we can even call it that, is flat (it's just a constant-time lookup into a cell index which involves some arithmetic). As a result it's fairly easy to apply optimization strategies similar to ray packets that Intel applies to their raytracing kernel/BVH (Embree) to test for multiple coherent rays simultaneously (in our case they would be "agent packets" for collision), except without such fancy/complex code since grid "traversal" is so much simpler.

On Memory Use and Efficiency

I covered this a bit in part 1 on efficient quadtrees, but reducing memory use is often the key to speed these days since our processors are so fast once you get the data into, say, L1 or a register, but DRAM access is relatively so, so slow. We have so precious little fast memory still even if we have an insane amount of slow memory.

I think I'm kind of lucky starting off at a time when we had to be very frugal with memory use (though not as much as the people before me), where even a megabyte of DRAM was considered amazing. Some of the things I learned back then and habits I picked up (even though I'm far from an expert) coincidentally aligns with performance. Some of it I've had to discard as bad habits which are counter-productive these days and I've learned to embrace waste in areas where it doesn't matter. A combination of the profiler and tight deadlines helps keep me productive and not end up with priorities that are too out of whack.

So one general piece of advice I'd offer on efficiency in general, not just spatial indexes used for collision detection, is watch that memory use. If it's explosive, chances are that the solution won't be very efficient. Of course there's a grey zone where using a bit more memory for a data structure can substantially reduce processing to the point where it's beneficial only considering speed, but a lot of times reducing the amount of memory required for data structures, especially the "hot memory" that is accessed repeatedly, can translate quite proportionally to a speed improvement. All the least efficient spatial indexes I encountered in my career were the most explosive in memory use.

It's helpful to look at the amount of data you need to store and calculate, at least roughly, how much memory it should ideally require. Then compare it to how much you actually require. If the two are worlds apart, then you'll likely get a decent boost trimming down the memory use because that'll often translate to less time loading chunks of memory from the slower forms of memory in the memory hierarchy.