# Deriving OBB to OBB Intersection and Manifold Generation

The Oriented Bounding Box (OBB) is commonly used all over the place in terms of simulation and game logic. Although often times OBBs aren’t actually bounding anything, and really just represent an implicitly defined box.

OBBs commonly come in 2D and 3D form, though there aren’t all that many resources on the internet describing how to derive the 3D case. The ability to write robust collision detection, and more importantly manifold generation (contact data needed to resolve the collision), relies on a good understanding of linear algebra and geometry.

Readers will need to know about affine and linear transformations, and have intuition about the dot and cross products. To learn these preliminaries I highly recommend the book “Essential Mathematics” by Jim Van Verth. I will also be assuming readers are familiar with the Separating Axis Theorem (SAT). If you aren’t familiar with SAT please do a short preliminary research on this topic.

The fastest test I’m aware of for detection collision between two OBBs, in either 2 or 3 dimensions, makes use of the Separating Axis Theorem. In both cases it is beneficial to transform one OBB into the space of the other in order to simplify particular calculations.

Usually an OBB can be defined something like this in C++:

## 6 Face Axes

As with collision detection against two AABBs, OBBs have x y and z axes that correspond to three different potential axes of separation. Since an OBB has local oriented axes they will most of the time not line up with the standard Euclidean basis. To simplify the problem of projecting OBB $$A$$ onto the the axes of OBB $$B$$, all computations ought to be done within the space of $$A$$. This is just a convention, and the entire derivation could have been done in the space of $$B$$, or even in world space (world space would have more complex, and slow, computations).

The translation vector $$t$$ is defined as $$t = R_{a}^{T} * (C_b – C_a)$$, where $$C_i$$ denotes the center of an OBB, and $$R$$ denotes the rotation of an OBB. $$t$$ points from $$A$$’s center to $$B$$’s center, within the space of $$A$$. The relative rotation matrix to transform from B’s frame to A’s frame is defined as $$C = R_{a}^{T} * R_b$$. $$C$$ can be used to transform $$B$$’s local axes into the frame of $$A$$. In $$A$$’s own frame it’s local axes line up with the standard Euclidean basis, and can be directly computed with the half width extents $$e_a$$.

To calculate the separation $$s$$ along the local axis $$l$$ from an OBB examine the following diagram and matching equation:

s = |t \cdot l| – (|a \cdot l| + |(C * b) \cdot l|)
\label{eq1}

Due to symmetry the absolute value of a projection can be used in order to gather a value that can be used to represent a projection along a given direction. In our case we aren’t really interested in the sign of the projection and always want the projection towards the other OBB. I understand that newer programmers may have some trouble translating math into code, so here’s an example of eq.\eqref{eq1} as C++:

## 9 Edge Axes

In 2D eq.\eqref{eq1} is all that is needed to detect overlap upon any given possible axis of separation. In 3D a new topological feature arises on the surface of geometry: edges. Because edges form entirely separate Voronoi regions from face surfaces they can also provide possible axes of separation. With OBBs it is fast to test all possible unique edge axes of separation.

Given two edges upon two OBBs in 3D space the vector perpendicular to both would be the possible axis of separation, and can be computed with a cross product. A possible 9 axes of separation arise from crossing each local axis of $$A$$ with the each local axis of $$B$$.

Since $$C$$ is composed of a concatenation between $$R_{a}^{T}$$ and $$R_b$$, the rows of this matrix can be thought of as the local axes of $$B$$ in the space of $$A$$. When a cross product between two axes is required to test separation, the axis can be pulled directly out of $$C$$. This is much faster than computing a cross product in world space between two arbitrary axis vectors.

Lets derive a cross product between $$A$$’s x axis and $$B$$’s z axis, all within the space of $$A$$. In $$A$$’s space $$A$$’s x axis is $$\begin{Bmatrix}1 & 0 & 0\end{Bmatrix}$$. In $$A$$’s space $$B$$’s z axis is $$\begin{Bmatrix}C_{20} & C_{21} & C_{22}\end{Bmatrix}$$. The axis $$l$$ is just the cross product of these two axes. Since $$A$$’s x axis is composed largely of the number $$0$$ elements of $$C$$ can be used to directly create the result of the cross product, without actually running a cross product function. We arrive to the conclusion that for our current case $$l = \begin{Bmatrix}0 & -C_{22} & C_{21}\end{Bmatrix}$$. This particular $$l$$ axis can be used in eq.\eqref{eq1} to solve for overlap.

Since the matrix $$C$$ is used to derive cross product results the sign of the elements will be dependent on the orientation of the two OBBs. Additionally, the cross product will negate on of the terms in the final axis. Since eq.\eqref{eq1} requires only absolute values it may be a good idea to precompute abs($$C$$), which takes the absolute value element by element. This computation can be interleaved between the different axis checks for the first 6 checks, only computed as needed. This may let an early out prevent needless computation.

The same idea can be used to derive this axis in the space of $$B$$ by crossing $$B$$’s local z axis with $$A$$’s local x axis transformed to the space of $$B$$. To do this it may be easy to use $$C^T$$, which represents $$R_{b}^{T} * A$$. More information about this can be found in my previous article about the properties of transposing a matrix concatenation.

Eventually all axes can be derived on paper and translated into code.

## Manifold Generation

A collision manifold would be a small bit of information about the nature of a collision. This information is usually used to resolve the collision during physical simulation. Once an axis of least penetration is found the manifold must be generated.

For SAT manifold generation is quite straight forward. The entirety of the details are expressed very well by Dirk Gregorius’ great online tutorial from GDC 2013. The slides are openly available for free, and currently hosted here generously by Erin Catto. Readers are encouraged to familiarize themselves with the idea of reference and incident faces, as well as the idea of reference face side planes. For a 2D analogue I have provided my own slides on this topic.

There are only two different cases of collision that should be handled: edge to edge and something to face. Vertex to edge, vertex to face, and vertex to vertex resolutions are ill-defined. On top of this numerical imprecision makes detecting such cases difficult. It makes more sense in every way to skip these three cases and stick to the original two.

## Something to Face

Something has hit the face of one of the OBBs. It can be anywhere from 1 to 4 vertices from the face of the opposing OBB. The entire intersecting volume between two OBBs does not need to be considered due to the nature of SAT and the axis of minimum penetration. Only two faces from each OBB need be considered for this case, and are by convention named the reference and incident faces, one from each OBB.

Finding the incident face given an axis of separation can be found in constant time with implicit geometry like an OBB; the problem reduces to that of finding a vector that most faces a given vector, or in other words the most negative dot product.

Once the incident face is identified it is clipped to the side planes of the reference face. There may be some tricks involving basis transformations here to simplify the clipping routine. In order to perform clipping the incident face vertices must be calculated. Given a face normal from an OBB all four face vertices can be computed with the normal and the half width extents of the OBB. In similar fashion the side planes of the reference face can also be directly computed.

## Edge to Edge

The edge to edge case requires a clever constant-time computation of a supporting edge. A supporting edge is the edge most pointed to by a given axis of separation. The computation of this edge may be tricky to derive yourself, though there do exist online references. I suggest checking out Open Dynamics Engine (ODE) by Russell Smith, or the Cyclone Engine by Ian Millington for information on how to compute a supporting edge.

Once both supporting edges are calculated the closest two points between each edge can be calculated and used as a single contact point of which to resolve upon.

## Numeric Stability

In all uses of OBB to OBB intersection cross products with nearly parallel vectors pose a problem. The best solution is to detect when any two axes from each OBB are parallel and skip all nine cross product axes. It can be visually shown that no false positives will come from skipping the cross product axes in this case.

A simpler solution, as proposed by Gottschalk ’00 in his paper “Collision Queries using Oriented Bounding Boxes”, which made its way into Ericson’s book “Real-Time Collision Detection”, is to bias all elements of $$C$$ with a small epsilon to drive near-zero axes away from a degenerate case. For other axes the epsilon value should be tuned such that it doesn’t have much an impact upon results.

If using this collision routine to resolve collision during physical simulation certain tuning my be appropriate. If an iterative resolver is used it may be preferred to have slightly more consistent manifold generation, as opposed to exact manifold generation. For details I suggest researching works by Erin Catto and comments made by others around the Bullet Forums.

## AABB to OBB

Hopefully by now readers realize that this whole time we’ve been simplifying the problem of OBB to OBB to the problem of AABB to OBB. When an actual AABB needs to be collided against an OBB simplifications can occur. Many simplifications revolve around the non-necessity to transform things into the basis of the AABB, since world coordinates should suffice.

## One Axis Aligned (or Two?)

Some games have OBBs that only orient on some of their axes instead of all of them. One can immediately realize that no cross product checks need to be performed in any case, if two OBBs have orientation locked on the same axis. Other optimizations will occur as well, making some axis tests reduced to that of AABB overlap testing.

## Conclusion

Collision detection is difficult. Writing robust collision detection code requires a good mathematical foundation as well as the ability to write efficient code. This slight mixture of fields of study may be difficult to learn all at once, but it all gets easier with time and practice and the rewards can be very fulfilling.

I’d like to thank my friend Nathan Carlson for teaching much of this information to me, and for his insightful discussions.

# Buoyancy, Rigid Bodies and Water Surface

I’ve spent the last couple weeks researching eigenvalue decomposition and solving cubic polynomials in order to simulate a liquid surface and polyhedral buoyancy! I gave a lecture about this topic at my university and hope the slides can be of interest or help to someone. I’ve attached a couple demo GIFs to the bottom of this post.

# Dynamically Slicing Shapes

Just this morning I finished up a small open source project, along with a huge article at gamedev.tutsplus. The open source project is an implementation of Sutherland-Hodgman clipping which can be used in many advanced shape and mesh manipulation techniques.

Gif of the open source project in action. Slicing a quad interactively.

# Orientation Constraint Derivation

I decided today that I wanted to create an orientation constraint. This will let me build more complex joints that make use of constraining relative angles between rigid bodies. I learned the details of implementing such a constraint from Bart Barenbrug’s thesis.

The position constraint is quite simple. The idea is to take a basis of vectors (u, v, w) into local space of two rigid bodies A and B. This yields xa, xb, ya, yb, za and zb. Each frame the local basis vectors are transformed into world space. These two world space sets should be aligned at all times in order to satisfy the constraint.

The dot product of xa and yb provides information about the error along u. The dot product of xa and zb provides information about the error along v. The dot product of ya and zb provides information about the error along w. Knowing this four vectors can be used instead of six, as the only ones needed are: xa, ya, yb, zb.

C = \theta_a – \theta_b = 0
\label{eq1}

Deriving the equation yields:

\dot{C} = \omega_a – \omega_b = 0
\label{eq2}

Resulting in the following Jacobian:

J = \begin{bmatrix} 0 & -t & 0 & t \end{bmatrix}
\label{eq3}

Where t is an axis of rotation within a basis matrix. Three of these constraints can be used to restrict each axis of rotation.

The interesting part of this constraint, for myself at least, was writing down on paper the mass matrix used to solve all three constraints simultaneously. All three constraints meaning restricting the relative rotation between two rigid bodies, aka the orientation joint (or angle joint). As a quick refresher the mass matrix is within the following equation:

\lambda = \frac{-(JV + B)}{J * M^{-1} * J^T}
\label{eq4}

The bottom portion J * M-1 * JT would be the constraint mass. This is actually a tensor and must be transformed from constraint space by using the tensor transformation formula. This is the same sort of transformation used on inertia tensors to transform them from model to world space.

Once this mass matrix is calculated it can be inverted and used to solve for lambda, where lambda will satisfy all constraint equations used in the mass matrix.

M^{-1} = \begin{bmatrix}
M^{-1}_a & 0 & 0 & 0 \\
0 & I^{-1}_a & 0 & 0 \\
0 & 0 & M^{-1}_b & 0 \\
0 & 0 & 0 & I^{-1}_b
\end{bmatrix}
\label{eq5}

Knowing this we can setup our constraint mass like so:

\begin{bmatrix}
0 & -t_1 & 0 & t_1 \\
0 & -t_2 & 0 & t_2 \\
0 & -t_3 & 0 & t_3 \\
\end{bmatrix}
\begin{bmatrix}
M^{-1}_a & 0 & 0 & 0 \\
0 & I^{-1}_a & 0 & 0 \\
0 & 0 & M^{-1}_b & 0 \\
0 & 0 & 0 & I^{-1}_b
\end{bmatrix}
\begin{bmatrix}
0 & 0 & 0 \\
-t_1 & -t_2 & -t_3 \\
0 & 0 & 0 \\
t_1 & t_2 & t_3 \\
\end{bmatrix}
\label{eq6}

Multiplying the two equations on the left gives:

\begin{bmatrix}
0 & I^{-1}_a * -t_1 & 0 & I^{-1}_b * t_1 \\
0 & I^{-1}_a * -t_2 & 0 & I^{-1}_b * t_2 \\
0 & I^{-1}_a * -t_3 & 0 & I^{-1}_b * t_3 \\
\end{bmatrix}
\begin{bmatrix}
0 & 0 & 0 \\
-t_1 & -t_2 & -t_3 \\
0 & 0 & 0 \\
t_1 & t_2 & t_3 \\
\end{bmatrix}
\label{eq7}

Multiplying the last two equations leaves the final constraint mass matrix:

\begin{bmatrix}
\scriptsize -t_1 \cdot I^{-1}_a -t_1 + t_1 \cdot I^{-1}_b t_1 &
\scriptsize -t_2 \cdot I^{-1}_a -t_1 + t_2 \cdot I^{-1}_b t_1 &
\scriptsize -t_3 \cdot I^{-1}_a -t_1 + t_3 \cdot I^{-1}_b t_1 \\
\scriptsize -t_1 \cdot I^{-1}_a -t_2 + t_1 \cdot I^{-1}_b t_2 &
\scriptsize -t_2 \cdot I^{-1}_a -t_2 + t_2 \cdot I^{-1}_b t_2 &
\scriptsize -t_3 \cdot I^{-1}_a -t_2 + t_3 \cdot I^{-1}_b t_2 \\
\scriptsize -t_1 \cdot I^{-1}_a -t_3 + t_1 \cdot I^{-1}_b t_3 &
\scriptsize -t_2 \cdot I^{-1}_a -t_3 + t_2 \cdot I^{-1}_b t_3 &
\scriptsize -t_3 \cdot I^{-1}_a -t_3 + t_3 \cdot I^{-1}_b t_3 \\
\end{bmatrix}
\label{eq8}

I apologize for the tiny text! Using this mass matrix like the following can be done to implement an orientation constraint to restrict the relative rotation of two rigid bodies:

# Protip: How to be a Physics Dev – Debug Rendering

Today I figured out the single most important tip I could ever give anyone in regards to being a competent physics engine developer. I will share this tip right here on my humble blog:

Your skills as a physics developer will grow at a rate proportional to how good your debug draw information is. I’d actually argue that your skills as a physics developer are proportional to how good your debug draw is.

I feel I’m at a decent place in my skills, as I feel my debug draw has become decent. Lets get some screenshots going!

Initial simplex during Quick Hull.

Stack of polyhedron (OBB meshes). The blue and red contacts are warm started (red is warm started consistently over many frames).

Spent a lot of time creating Maya-like camera controls with orbiting.

I digress. I didn’t figure out this simple fact on my own; I gleaned this wisdom off of my friend Nathan Carlson. He pointed out that the reason a lot of students at DigiPen have trouble with computational geometry and general physics engine development due to a lack of debug rendering.

Just take a look at Dirk Gregorius’s sample demo from his GDC 2013 lecture. He fully renders Gauss maps, a minkoswki difference, and has glorius camera controls. I actually copied his camera design and implemented it myself. You can see that he has Ant Tweakbar integrated as well, for various tweaking tasks. Erin Catto has lots and lots of different tests, asserts, and most important of all tons of debug drawing for his Box2D library. The Zero team on the third floor of DigiPen definitely has the craziest debug draw I’ve ever heard of!

Hopefully I can achieve something at least in the likeness of all these other developers, and I recommend readers do the same; intuition is often best built visually when it comes to geometry.

# Dynamic AABB Tree

After studying Box2D, Bullet and some slides an alumnus (thanks to Nathan Carlson!) from DigiPen created, I put together a dynamic AABB (axis aligned bounding box) tree broadphase. A Dynamic AABB Tree is a binary search tree for spatial partitioning. Special thanks to Nathanael Presson for the original authoring of the tree within the Bullet engine source code (see comments of this post).

A broadphase in a physics simulation has the job of reporting when bodies are potentially colliding. Usually this is done through cheap intersection tests between simple bounding volumes (AABBs in this case).

There are some interesting properties of a dynamic AABB tree that make the data structure rather simple in terms of implementation.

There are a couple main functions to implement outlined here:

• Insert
• Remove
• Update

The underlying data structure ought to be a huge array of nodes. This is much more optimal in terms of cache performance than many loose heap-allocated nodes. This is very important as the entire array of nodes is going to be fetched from memory every single time the broadphase is updated.

The node of the AABB tree can be carefully constructed to take up minimal space, as nodes are always in one of two states: branches and leaves. Since nodes are stored in an array this allows for the nodes to be referenced by integral index instead of pointer. This allows the internal array to grow or shrink as necessary without fear of leaving dangling pointers anywhere.

The idea of the tree is to allow user data to be stored only within leaf nodes. All branch nodes contain just a single AABB enveloping both of its children. This leads to a short description of invariants for the data structure:

• All branches must have two valid children
• Only leaves contain user data

The first rule allows for some simplification of operations like insert/remove. No additional code branching is required to check for NULL children, increases code performance.

## Insert

Insertion involves creating a fat AABB to bound the userdata associated with the new leaf node. A fat AABB is just an AABB that is slightly larger than a tight fitting AABB. Once a new leaf node is created with its fat AABB a tree traversal is required in order to find a sibling to pair up with, and a new parent branch is constructed.

Traversing the tree should be done by following some sort of cost heuristic. The best seems to be a cost involving the surface area of involved nodes. See Box2D for a resource in cost heuristics.

After a new parent is created and a sibling found, the bounding volume hierarchy of all parent nodes are invalid. A traversal up the tree, correcting all the bounding AABBs and node heights is required. This hierarchy correction can be abstracted into a simple function:

## Remove

Removing nodes from binary search trees can involve a stack to trace back a traversal path, however due to the few invariants of the dynamic AABB tree removal quite simple. Since all branches must contain two valid children, and only leaves contain userdata, the only nodes that require deletion are leaves.

Remove the leaf’s parent and replace it with the leaf’s sibling. After this operation the parent hierarchy needs to be recalculated (just as in insert).

## Update

Since this tree is dynamic it needs a way to handle moving objects, and not just static level geometry. Since fat AABBs are created upon insertion, objects can move around a little bit before the AABB within the tree is invalid. The fatness factor, or how much to fatten each AABB, can be fine-tuned for performance. I myself use an arbitrary distance (about 5% of the average scale of objects). It is also possible to fatten the AABB based upon the object’s previous frame’s displacement (see Box2D for details on displacement predictions).

The update function of the tree checks to make sure that the current tight-fitting AABB of a shape is still contained by the AABB within the tree. In order for this operation to be constant time the node index in the tree needs to be intrusively stored within the shape itself. This will allow a shape to provide its node index along with an up to date tight fitting AABB to the tree, in order to see if its AABB is still within the fat AABB’s bounds.

If the tight AABB is not contained by the fat AABB the shape needs to be removed and reinserted into the tree.

## Balancing the Tree

Since this sort of spatial partitioning involves a binary search tree, the tree will perform optimally (in terms of collision queries) so long as the tree is balanced.

However, how you balance the tree matters. I’ve heard (through rumors) that dynamic AABB trees ought to be balanced in terms of surface area and not tree height. Though this makes sense conceptually, I was not sure how to balance the tree based of surface area. Knowing this, I just went with something I’m more comfortable with, which is balancing based upon tree height.

Balancing a single node involves taking a look at its two children. The height of each child should be compared, and if one is higher than the other by a height of two or more a rotation should be performed. In other words, the child with a larger height ought to be promoted, wherein promotion is a way of rotating a node up the tree hierarchy.

This sort of balancing (AVL balancing) can be performed at the beginning of the hierarchy synchronization loop. This allows the tree to self-balance itself upon insertion and removal of leaves.

## Free List

The tree needs to maintain an internal free list of unused nodes. Constructing a free list involves looping over all the nodes of the tree, upon initial tree construction, linking each node to the next with indices. This process is really simple, though readers may be unfamiliar with lists of indices as opposed to lists of pointers.

Here is a helper function I’ve constructed for myself to setup free lists (useful upon construction of the tree, and growing of the internal array):

Taking nodes from the free list involves setting the current m_freeList index to the next node in the list. Inserting nodes is just as trivial. You can think of this sort of list as a singly linked list, except the links are through indices rather than pointers.

## Queries

Querying volumes against the tree is highly efficient, so long as collision checks against an AABB are fast. Sphere, AABB and raycasts are all very fast.

Querying the tree involves detecting collisions with each node in the tree, starting with the root, traversing to all children until the tree is exhausted. Traversal to a child should be performed if there is a collision with the parent.

This sort of query can easily be done with recursion (note: the callback returns bool, signifying continue or termination of the query):

However it may be more favorable to do so with an iterative algorithm. Iterative algorithms are generally a little harder to implement, but take much less memory (and thus are more efficient):

## Culling Queries Further

It might be a good idea to only query rigid bodies that moved last frame. This is much better than querying all dynamic bodies unconditionally. If you have a contact graph or constraint graph, or any sort of islanding implementation then you can easily cull objects from queries. The idea is that if a rigid body is static or not placed within an island, then it didn’t move at all the previous frame. This is very simple and elegant.

Usually only active (awake) dynamic bodies are placed into an island (and any objects they have contact constraints with). This is because objects that are sleeping don’t need to be considered at all when forming new islands, as they are asleep. Knowing this a small flag can be set within a shape when it is placed into an island. This flag can later be checked to know whether or not it needs to be queried at all within the broadphase.

Though this step is not specific to the dynamic AABB tree, it is a nice trick to know. One caveat is that when contact constraints are updated, they must first have each involved shape’s bounding volume checked against each other. This will allow old contact constraints to be removed once there is no potential for the narrow phase to return true.

## Conclusion

The dynamic AABB tree is an extremely fast spatial partitioning data structure, and is ideal for both large unbounded worlds, and lots of dynamic rigid bodies. Querying the tree has the time complexity of a binary search. I can’t imagine much better of a broadphase for rigid body simulation. There’s an old saying of “no broadphase is perfect, and neither is this one.” when speaking of broadphases. However the dynamic AABB tree has completely impressed me.

In terms of performance, my simulation would grind to a halt and bottleneck with an N^2 broadphase at just over 100 dynamic bodies. Now I can easily simulate around 5000 rigid bodies with the broadphase taking about 5% of the simulation time. My current bottleneck is cache misses during constraint solving.

# Separating Axis Test and Support Points in 2D

I’ve created some slides for Physics Club at DigiPen. Currently the slides were made at my own house with my own resources, so DigiPen doesn’t own them. Thus, I can share this version for public viewing!

The Separating Axis Test (SAT) is a highly versatile and robust collision detection algorithm, and can be implemented in an extremely efficient manner in 2D without too much trouble. I hope these slides can help others out with their collision detection and manifold generation problems.

# Introduction

During the previous article in this Custom Physics Engine series we talked about impulse resolution. Though understanding the mathematics and physics presented there are important, not much could be put into practice without both collision detection and manifold generation.

Collision detection is a pretty widely documented area, and so I won’t go into too much detail on how to achieve collision detection in this article series. Instead I’ll focus on manifold generation, which is in my opinion much more difficult and less-documented compared to collision detection.

In general collision detection is useful for retrieving a boolean result of “are these two things colliding”. The usefulness of such a result ends when this collision needs to be resolved. This is where manifold generation comes in.

# Manifold Generation – Summary

A manifold, in context of physics engines, is a small structure that contains data about the details of a collision between two objects. The two bodies are commonly referred to as A and B. Whenever referring to a “collision” as a system, A is usually the reference object, as in the problem is viewed from A’s orthonormal basis.

I am not actually sure why this structure is called “the manifold”, and I do not know anyone that actually knows. So don’t ask! Either way this structure should be passed around by reference or pointer to avoid unnecessary copies. I also pool all my manifolds, and intrusively link them in order to keep a list of active manifolds during a scene’s step (the term scene is defined in the previous article).

Manifold generation involves gathering three pieces of information:

• Points of contact
• Penetration depth
• Vector of resolution, or collision normal

The points of contact are 2D points (or 3D for a 3D engine) that mark where one shape overlaps another. Usually these contact points are placed onto the vertex of one shape that resides within another.

Two points of contact for two boxes found intersecting.

The penetration depth is the depth of which the two shapes are intersecting. This is found using the Separating Axis Test (SAT). There are lots of resources around that talk about SAT, so I suggest googling for them. The penetration depth is defined as the axis of least penetration. In this case (assuming blue’s frame of reference and vertical as y axis) the y axis is the axis of least penetration.

The collision normal is used to describe in which direction to press both objects away from one another. The collision normal will be a face normal, and in this case it would be a normal pointing towards the brown box, where the normal corresponds to the blue box’s top face.

Generating these three pieces of information can be quite a challenge. Now lets view what a possible setup of the manifold structure might look like:

Note that there can be a variable amount of contact points. I suggest having a contactCount of zero signify “no collision”. I also suggest having only two possible points of contact for a 2D simulation, and to start just use a single possible point of contact. More than one point of contact isn’t necessary until advanced constraint resolution is used (a future article).

It is important to just keep an array of contacts within this data structure as to keep strong cache coherency. There’s no reason to dynamically allocate the array of contacts.

# Circle to Circle

I’ll be covering how to gather manifold information for specialized cases of couple different types of shapes. Lets first go over circle to circle first. Here is what the definition of a circle would look like:

The first thing to do is to see if they are colliding or not. Again, throughout this article I’m going to mostly blaze through the collision detection and focus just on gathering important manifold information. Feel free to google or ask specific questions in the comments about collision detection.

The above code is really quite simple. The important thing to note is that our contact normal will always be a vector from A to B. In order to create a vector from one point to another you take your endpoint minus your starting pointer. In this case B’s position subtracted by A’s position. This results in a vector from A to B. This vector normalized will be the collision normal, or in other words the direction in which to resolve the collision.

It is important to note that no square root functions are called before the early out condition is checked. Most of the time your shapes are probably not colliding, and so there’s no reason to use a square rooted value.

The last tricky thing is to check if the two shapes are right on top of each other. Though this is unlikely in a dynamic environment, sometimes shapes can be placed directly upon one another through an editor. It is important to, in all collision detection functions, to handle all special cases, even if the handling is bad. Whatever you do just make sure you are consistent. I just chose a random vector to resolve in the direction of.

The nice thing about cirlce to circle collision is that only one collision point is really needed. Just  be sure to be consistent in how you choose your collision point in order to reduce simulation jitter.

# AABB to AABB

Collision detection between two AABBs is a bit more complicated than two circles but still quite simple. The idea is to make use of min and max. Lets assume we’re storing our AABBs in a structure like so:

This allows a very simple algorithm to find points of contact. For convex polygons that are not axis aligned often times Sutherland-Hodgman clipping will need to be performed. In our case we can implicitly deduce our contact area due to the nature of AABBs.

First determine if the two AABBs are overlaping at all. When an axis of least penetration is found the collision area can then be deduced.

AABB intersection with intersection points and area of intersection. Min the maxes and max the mins.

The idea is to perform the SAT while storing each overlap value. The least overlap is your axis of separation. To get the contact area and two points of intersection you can min your maxes and max your mins (I’m talking about the extents of each AABB).

This sounds silly, but that’s how you do it. I suggest drawing it out. Here’s how to find your collision area given by two points (intersection points of the AABBs):

The last bit of info required would be to record the penetration and contact normal. Penetration is your axis of least overlap, so after you’ve found your axis of least overlap you can just assign a vector value as your contact normal. If you have found the axis of least penetration to be on the x axis, you want to point towards object B along the x axis. If the y axis is the axis of least penetration, you want to point towards object B along the y axis.

That’s all there is to the AABB to AABB intersection. Be sure to properly record the number of contacts found (if any), and if neither axis x or y are actually overlapping, then that means there is no intersection.

# AABB to Circle

I will leave AABB to Circle collision an exercise for the reader, though I will quickly provide an explanation paragraph behind the idea. What needs to be done is to first determine if the shapes are overlapping at all. I have a previous post on my blog that explains the Circle to AABB intersection, and more information about such an intersection check can be found around the internet.

Lets assume A is the AABB and Circle is B and we have a collision. The collision normal will again be the vector from A to B, except slightly modified. The early out condition involves finding the closest point on the AABB to the Circle. The collision normal is the translation vector from A to B subtracted by a vector to the closest point on the AABB. This will represent a vector from the circle’s center to the closest point.

The contact point will be residing on the circle’s radius in the direction of the contact normal. This should be easy to perform if you understood the Circle vs Circle collision detailed above. The penetration depth will be the length of the collision normal before it is normalized.

There is one special case that must be properly handled: if the center of the circle is within the AABB. This is quite simple to handle; clamp the center of the circle to the edge of the AABB along the edge closest to the circle’s center. Then flip the collision normal (so it points away from the AABB instead of to the center) and normalize it.

# OBBs and Orientation

Now lets start talking about adding in some manifold generation for some more complex oriented shapes! The first thing that must be learned is how to properly change from one orthonomormal basis to another (that is shift from one frame of reference to another). This will vastly simplify collision detection involving OBB shapes.

Changing a basis involves taking the orientation and translation of an OBB and applying the inverse of these two it to another shape. In this way you can then treat the OBB as an AABB as long as you are still referring to your transformed object. Lets go over this in some more detail with some pictures.

Here is what an OBB is like in the OBB’s frame of reference (left), and the OBB in model space (right).

Note: origin is (0, 0) in model and reference space.

The important thing to realize is that in order to place an object into an OBB’s frame of reference it must have inverse translation and rotation of the OBB’s translation and rotation applied to it. This takes the OBB’s position to the origin, and the OBB can then be treated as an AABB.

If the inverse rotation of the OBB, in this case -45 degrees, is applied to both the OBB and an object near it, this is what happens:

Change of basis going from left to right.

As you can visually see, once the circle has been inversely transformed into the OBB’s frame of reference the OBB can be viewed as a simple AABB centered at the origin. The extents of the OBB can be used to mimic an AABB, and the OBB to Circle intersection and manifold generation can be treated identically to the AABB to Circle intersection, if a proper inverse transformation is performed. Again, this inverse transformation is called a “change of basis”. It means you transform the Circle into the OBB’s frame of reference.

# Mat2 Rotation Matrices

Lets go over rotations in 2D extremely quickly. I won’t go over derivation here for brevity’s sake (as you will see, brevity is a close friend of mine in these Physics Engine articles haha). Instead I will just show you how to create your own 2 by 2 matrix and use it as apart of whatever math library you currently have (which you should hand-code yourself!). Really the only useful thing about having a 2 by 2 matrix is to do rotation operations.

For those using C++ you’re in luck for I know how to use unions.

The above is a proper usage of the unnamed union trick. The elements of the 2 by 2 array can be accessed as if they are a two dimensional array, single dimensional array, or separate floating point values. Additionally you can stick two vectors into your union for column or row access, if you so wish.

I want to briefly hit all the important methods without writing an entire book, so get ready for code snippets to be thrown at you.

The first thing you should realize is that the default constructor does nothing. This is important. Often times you will create a matrix only to briefly thereafter assign some value to it. Do not default construct your matrix to zero values as an optimization. Force the user to use the Set function like so: mat.Set( 0, 0, 0, 0 ).

The interesting functions here are the rotation constructor and SetRotation functions. Each one computes cosine and sine from a given radian value and caches the result. Caching the results prevents unneeded additional calls to cosine and sine. Note the format in which sine and cosine are stored. It is also important to realize that m00 and m10 represent a transformation of the x axis, and m01 and m11 represent a transformation of the y axis. Each of these two are columns, both columns can be viewed as unit vectors.

Multiplying a Mat2 with a vector will rotate the vector’s x and y components around the origin. It is important to realize where your origin is before you apply a rotation. If you want to jump into an OBB’s frame of reference you must do an inverse translation to set the OBB as the origin. This allows you to then apply the OBB’s inverse rotation (perhaps with the inverse operator of your Mat2, see Box2D if you don’t know how to inverse a Mat2) and rotate about the origin (which is about the OBB).

# OBB Representation

Every oriented shape will need to store its orientation somehow. I suggest the following:

The OBB should store its current orientation in both a single floating point value along with a matrix to represent that radian value as a rotation matrix. When you need to rotate the OBB during integration, you can just add or subtract a little bit to the radians value, and then call u.SetRotate( radians ) to update the rotation matrix. This makes use of a simple and organized way to cache results from sine and cosine calls, and minimizes the amount of calls to these functions that you require.

# OBB to OBB

Now lets talk about the big one. How in the world can you see if two OBBs intersect? Both boxes are oriented, so the problem would involve a lot of complex calculations involving trigonometric computations.

Lets make things easier: transform one OBB into the other OBB’s frame of reference, and treat the transformed object as an AABB. Now the problem becomes much simpler.

First perform a separating axis check and find the axis of least penetration. In order to perform the SAT you must find a projected radius onto the axis you are currently testing.

If the sums of the projected radii from both OBBs are larger than the distance between the center of each OBB (along your respective axis), then they are intersecting on that axis. This method works for all convex polygons in 2D.

The way I perform this check is by taking the translation vector from A to B, lets call it T. Then I rotate T into A’s frame of reference and subtract A’s extent vector, and subtract that entire result by   B’s extent vector rotated into A’s frame of reference. This results in a vector holding the overlap along the x and y axis for object A. Due to symmetry only two axes need to be tested. The same operation can be done for object B to find B’s separating axis. If no separating axis is found the shapes are intersecting.

This is where things start to get difficult. Since we just performed an early out test, now we need to find the axis of least separation. However you cannot just blindly perform floating point comparisons due to floating point error during rotation of the translation vector from A to B. You must bias your comparisons to favor one axis over another in a consistent manner. This is important for stability!

In the above picture, which set of contact points/normal is the “correct” one? Each axis of separation is very close to the same distance, so floating point error could account for which axis is chosen. If your simulation flops between the two you’ll end up with strange jitter and your simulation will be less believable. The solution is to just favor one axis over another using an error EPSILON value.

Here’s a function (you’re lucky I just gave it to you!) that will check which value is greater than the other. Each value is modified slightly, and a small bias is fed into the comparison based off of how large each value passed in is. This can be used to favor one axis of separation over another, until a threshold larger than floating point error is breached.

Carefully record which direction your normal goes (from A to B) depending on what axis is separating. This is a similar operation to the one found in AABB to AABB as seen above.

Once an axis is found two line segments must be identified: the reference face and incident face. The reference face corresponds to your normal you recorded. So the reference face is the face that corresponds to your axis of least penetration. If your axis of least penetration is on A, then your  reference face is on A. The incident face is the one with the information we need to generate our manifold.

The incident face it the face on the other object that the reference face has hit. We must compute it. All that needs be done is find out which face is most facing the normal (has the most negative dot product). Looping through all faces performing dot products is the simplest way to achieve this. A more optimized algorithm is the follow the sign of the flipped normal. Your normal will have an x and y component (and z in 3D), and each component will be positive or negative. This gives you a total of four combinations of positive or negative.

First check to see if the normal is point more towards the x axis or y axis (after you transform it into the incident face’s frame of reference). Then check the sign of the y axis. You know know to which face your normal is most pointing.

Think of it this way: if the normal is pointing more towards the x axis (absolute value of n.x is greater than absolute value of n.y), then you are going to be pointing more towards either the left or right face on your OBB (in the OBB’s frame of reference). All that you need to know from there, is if you’re pointing left or right on the x axis, which is denoted by the sign of the normal.

Your incident face segment endpoints are the extents of the x axis of the OBB, which are aligned with the x axis in the OBB’s frame of reference. You can then take the OBB x half-extent and use the positive and negative version of it to form two points: (-x, 0) and (x, 0) where x is the half-extent of the OBB on its x axis. Rotate these points with the OBB’s rotation matrix, and then translate them into world space with the OBB’s position vector, and you now have your endpoints for your incident face in world space.

All that is left is the clip the incident face to the reference face side planes using Sutherland-Hodgman clipping. Here’s a diagram showing this:

This is a fairly difficult thing to do unless you know your math fairly well. Each side plane can be simply computed once you know your reference normal. Here’s the process for getting two side planes:

The above code was hand-derived by myself, but you’ll find something very similar within Box2D Lite (where I originally learned the math from). If this is confusing to you I suggest reading up on the various types of representations of lines in 2D.

Here’s another diagram I just found in my own source files:

You might have noticed I’m storing the c value. This is important as the c value stored within the Line structure can be used to find the distance of a point to the line like so:

I’ll be nice and provide you my clipping notes I created for Sutherland-Hodgman clipping :)

However since we are clipping a line to a single plane the algorithm will need to be slightly modified. In some cases you need to push extra points, since Sutherland-Hodgman assumes to be clipping two polygons in a loop. See Box2D Lite for a good implementation of the incident to line clipping. I however use my own hand-derived algorithm that works in a very similar way. I’ll share some pseudo code for clipping a segment to a Line, assuming the Line is in the format of offset c and a normal n:

After clipping to the side planes floating point error must be accounted for. If our clipping process went as expected we must have two resulting points. If we end up with less than two output points that means floating point error has screwed us over, and we must treat the entire process as if the two OBBs are non-intersecting.

Assuming we have two points output from our clipping we then need to only consider points that are behind the reference face. Use the DistanceToLine function I provided above to check this. Record each point behind the reference face as a contact point!

# OBB to AABB

If you’ve been reading along diligently and deriving your own understanding, you should be able to figure out how to perform such a check. This test is the exact same as OBB to OBB, except with less rotating from one basis to another. You can recall that the OBB to OBB test rotated one OBB into the frame of the other completely, turning the test into an AABB to OBB test. The same thing can be done here without the preliminary change of basis, and perhaps some other small optimizations. I will leave the section out as a challenge for the reader.

# Conclusion

I hope you have a solid understanding of various types of hand-crafted intersection tests! Feel free to email me or comment here with questions or comments. I refer you to Box2D Lite’s source code as a reference to the material in this article, along with all of Erin Catto’s GDC slides, especially the one from 2007. The next article in this series is likely to talk about creating a simple O(N^2) broadphase, and how to cull out duplicate contact pairs. Until then this article along with the previous one is more than enough to create a simple physics engine. Be aware that with impulse resolution only one point of contact should be necessary. This article goes over grabbing multiple contact points, and this is because more advanced collision resolution techniques can make use of more contact points.