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The barycentric conspiracy

February 6, 2013

This post is part of a series – go here for the index.

And welcome back to my impromptu optimization series. Today, we won’t see a single line of code, nor a profiler screenshot. That’s because our next subject is the triangle rasterizer, and we better brush up on our triangle facts before we dive into that. A lot of it is going to involve barycentric coordinates, hence the name. Be warned, this post is a lot drier than the previous ones, and it doesn’t even have a big pay-off at the end. It’s a pure, uncut info-dump. The purpose is to collect all this material in one place so I can refer back to it later as necessary.

Meet the triangle

Let’s just start by fast-forwarding through a whole bunch of things I assume you already know. If you don’t, Wikipedia can help you, as can virtually any maths textbook that covers planar geometry.

A triangle.

A triangle is a polygon with 3 vertices v0, v1, v2 and 3 edges v0v1, v1v2, v2v0. You can see a fine specimen on the left. A degenerate triangle is one where the three vertices are collinear, i.e. they all fall on the same line (or they might even be all the same point). Any triangle lies on a plane, and for all non-degenerate triangles, that plane is uniquely determined. This holds in any dimension, but is somewhat vacuous in 0D or 1D where any 3 vertices are going to be collinear.

Restricting ourselves to 2D for the moment, a triangle, like any non-self-intersecting planar polygon, divides the plane into two regions: the interior, which is finite, and the exterior, which is not. The two are separated by the boundary of the triangles, which consists of the three edges. To rasterize a triangle, we essentially just need to query a bunch of points, usually directly corresponding to the pixel grid or arranged in some other regular pattern, and figure out whether they are inside or not. Since this is a binary result and our query provides a ternary answer (outside, on the boundary, inside), we need to define how points on the boundary are to be handled. There’s multiple ways to do this; I’ll cover them in the article about actual triangle rasterization. Since a triangle is always planar, it’s easy to extend these definitions to higher dimensions by always working in the plane of the triangle (or in a plane of the triangle, should it be degenerate).

Finally, triangles are always convex: for any two points inside the triangle, the line connecting them is also fully inside the triangle. Convexity turns out to be important: in the plane, the inside of a convex polygon with n edges can always be written as the intersection of n half-spaces. That fact in itself is enough to write a triangle rasterizer, but if you haven’t done this before, you might be wondering what the hell I’m talking about at this point. So let’s take a step back and talk about the geometry of the situation for a second.

Oriented edges

Positive half-space of v0v1 Consider, for a moment, the edge v0v1 in the image above. The edge itself is a line segment. The corresponding line divides the plane into two halves (the half-spaces I mentioned): a “left” side and a “right” side. This is shown in the image on the left, with the “left” side being shaded. Now, speaking of it in those terms gets problematic if the edge we picked happens to be horizontal (which of the two halves is the “left” one if they’re stacked vertically?). So instead, we’re going to phrase everything relative to the edge rather than the picture we’re drawing: imagine you’re walking down the edge from v0 towards v1. Then, we’re gonna refer to everything on your left side (if you’re looking towards v1) as the “positive” half-space, and everything to the right as the “negative” half-space. Finally, points that are either right in front of you or right behind you (that is, points that fall on the line) belong to neither half-space.

Now, if we apply the same construction to the other two edges and overlay it all on top of each other, we get this image:

Three half-spaces at once

Walking through all its technicolored glory (apologies to the color-blind):

  • The green region is in the positive half-space (I’m just gonna write “inside”) of v1v2 and v2v0, but outside of v0v1.
  • The cyan region is inside v2v0, but outside the two other edges.
  • Blue is inside v0v1 and v2v0.
  • Magenta is inside v0v1 – all that’s left of the region we saw above.
  • Red is inside v0v1 and v1v2.
  • Yellow is inside v1v2.
  • Finally, the gray region is inside all three edges – and that’s exactly our triangle.

And so we have the picture to go with my “intersection of 3 half-spaces” comment earlier. This means that all we need to figure out whether a point is in a triangle is to figure out whether it’s in all three positive half-spaces, which turns out to be fairly easy. That is, assuming that such a point even exists – what would’ve happened if v2 had been to the right of v0v1 instead of to the left, or if the same thing happened with any of the other edges? We’ll get there in a minute, but let’s first go into how we figure out whether a point is in the positive half-space or not.

Area, length, orientation

If we have the coordinates of all involved points, the answer turns out to be: determinants. And not just any old determinant will do; given the three points a, b and c, we want to compute the determinant

\mathrm{Orient2D}(a,b,c) = \begin{vmatrix} a_x & b_x & c_x \\ a_y & b_y & c_y \\ 1 & 1 & 1 \end{vmatrix} = \begin{vmatrix} b_x - a_x & c_x - a_x \\ b_y - a_y & c_y - a_y \end{vmatrix}

Clearly, if this expression is positive, c lies to the left of the directed edge ab (i.e. the triangle abc is wound counter-clockwise), and with that out of the way, we can start rasterizing triangles…

Wait, what?

Sorry, pet peeve. A lot of texts like to just spring these expressions on you without much explanation. That’s fine for papers, where you can expect your audience to know this already, but even a lot of introductory texts don’t bother with an actual explanation, which annoys me, because while this isn’t hard, it’s by no means obvious either.

So let’s look at this beast a bit more closely. First, notice how the first expression simply puts the three vertices into the columns with an appended 1 – why yes, those are in fact homogeneous coordinates, thank you for noticing. We’re not gonna make use of that here, but it’s worth knowing. Second, because we just use the vertex coordinates as the columns, this should make it immediately obvious that this expression is the same for all possible orderings of a, b, c, up to sign (this is just a determinant identity). In particular, if we plug in in our vertex coordinates for a, b, c, we always get the same value (this time including sign) for all three cyclical permutations (v0v1v2, v1v2v0, and v2v0v1) of the vertices. Which in turn means that the “sidedness” we compute is going to be same for all three edges, answering one of our questions above.

Next, note that we can transform the first form (the 3×3 determinant) into the second form by subtracting the first column from the other two and then developing the determinant with respect to the third row, which should hopefully make it a bit less mysterious. There’s also a very nice way to understand this geometrically, but I’m not going to explain that here – maybe another time. Anyway, now that we know how to derive the 2×2 form, let’s look at it in turn. With arbitrary 2D vectors p and q, the determinant

\begin{vmatrix} p_x & q_x \\ p_y & q_y \end{vmatrix}

gives the (signed) area of the parallelogram spanned by the edge vectors p and q (I’m assuming you know this one – it’s a standard linear algebra fact, and proving it is outside the scope of this article). Similarly, a 3×3 determinant of vectors p, q, r gives the signed volume of the parallelepiped spanned by those three vectors, and in higher dimensions, a n×n determinant of n vectors gives the signed n-volume of the corresponding n-parallelotope, but I digress.

So, with that in mind, let’s first look at our triangle and try to compute Orient2D(v0, v1, v2). That should help us find out whether it’s wound counter-clockwise (i.e. whether v2 is to the left of the oriented edge v0v1) or not. The expression above tells us to compute the determinant

\begin{vmatrix} v_{1x} - v_{0x} & v_{2x} - v_{0x} \\ v_{1y} - v_{0y} & v_{2y} - v_{0y} \end{vmatrix}

which should give us the signed area of the parallelogram with edges v0v1 and v0v2. Let’s draw that on top of our triangle so we can see what’s going on:

Parallelogram for our triangle

Now, there’s two things about this worth mentioning: First, if we were to swap v1 and v2, we would get the same edge vectors, just in the opposite order – we swap two columns of the determinant, which flips the sign but leaves the absolute value untouched. Now, our original triangle is wound counterclockwise: the third vertex v2 is to the left of the first edge v0v1. If we swap v1 and v2, we get the same triangle, only this time the third vertex (now v1) is to the right of the first edge (now v0v2). More precisely, the sign of the determinant turns out to be positive if our first turn is counter-clockwise, and negative if our first turn is clockwise. If it’s zero, all three vertices are collinear, so the triangle is degenerate – also useful to know.

The second thing is that the parallelogram we’re looking at clearly has twice the area of the triangle we started with. This is no accident – constructing the fourth vertex of the parallelogram produces another triangle that is congruent to the first one, so the two triangles have the same area, hence the parallelogram has twice the area of the triangle we started out with. This gives us the standard determinant formula for the area of the triangle:

\mathrm{TriArea}(v_0,v_1,v_2) = \frac{1}{2} \begin{Vmatrix} v_{1x} - v_{0x} & v_{2x} - v_{0x} \\ v_{1y} - v_{0y} & v_{2y} - v_{0y} \end{Vmatrix}

The other standard formula for triangle area is \frac{1}{2} b h, where b is the length of the base of the triangle (=length of one of its edges) and h is the corresponding height (=length of the perpendicular of b through the vertex opposite b). In fact, the proof for this formula uses the same parallelogram we just saw. Compare the two expressions and we note that our signed area computation can be written

\triangle(v_0,v_1,v_2) := \frac{1}{2} \begin{vmatrix} v_{1x} - v_{0x} & v_{2x} - v_{0x} \\ v_{1y} - v_{0y} & v_{2y} - v_{0y} \end{vmatrix} = \frac{1}{2} \|v_1 - v_0\| \, h(v_2,v_0v_1)

where h(v2, v0v1) denotes the signed height of v2 over v0v1 – this isn’t standard notation, but bear with me for a minute. The point here is that the value of this signed area computation is proportional to the signed distance of v2 from the edge. That this works on triangles should not be surprising – the same is true for rectangles, for example – but it’s worth spelling out explicitly here because we’ll be doing a lot of signed area computations to determine what is in effect signed distances. So it’s important to know that they’re equivalent.

Edge functions

Now, let’s get back to our original use for these determinant expressions: figuring out on which side of an edge a point lies. So let’s pick an arbitrary point p and see how it relates to the edge v0v1. Throwing it into our determinant expression:

\begin{vmatrix} v_{1x} - v_{0x} & p_x - v_{0x} \\ v_{1y} - v_{0y} & p_y - v_{0y} \end{vmatrix} = (v_{1x} - v_{0x}) (p_y - v_{0y}) - (v_{1y} - v_{0y}) (p_x - v_{0x})

and if we rearrange terms a bit, regroup and simplify we get

F_{01}(p) := (v_{0y} - v_{1y}) p_x + (v_{1x} - v_{0x}) p_y + (v_{0x} v_{1y} - v_{0y} v_{1x})

This is what I’ll call the edge function for edge v0v1. As you can see, if we hold the vertex positions constant, this is just an affine function on p. Doing the same with the other two edges gives us two more edge functions:

F_{12}(p) := (v_{1y} - v_{2y}) p_x + (v_{2x} - v_{1x}) p_y + (v_{1x} v_{2y} - v_{1y} v_{2x})
F_{20}(p) := (v_{2y} - v_{0y}) p_x + (v_{0x} - v_{2x}) p_y + (v_{2x} v_{0y} - v_{2y} v_{0x})

If all three of these are positive, p is inside the triangle, assuming the triangle is wound counter-clockwise, which I will for the rest of this article. If it’s clockwise, just swap two of the vertices before you start hit-testing. Now, these are normal linear functions, but from their derivation and the determinant properties we saw earlier, we know that they in fact also measure the signed area of the corresponding parallelogram – which in turn is twice the signed area of the corresponding triangle. Let’s pick a point inside the triangle and draw the corresponding diagram:

Triangle with p inside Our original triangle is partitioned into three smaller triangles that together exactly cover the area of the original triangle. And since p is inside, these triangles are all wound counter-clockwise themselves: they must be, because these triangles have signed areas corresponding to the edge functions, and we know all three of them are positive with p inside. So that’s pretty neat all by itself.

But wait, there’s more! Since the three triangles add up to the area of the original triangle, the three corresponding edge functions should add up to twice the signed area of the full triangle v0v1v2 (twice because triangle area has the 1/2 factor whereas our edge functions don’t). Or, as a formula:

F_{01}(p) + F_{12}(p) + F_{20}(p) = 2 \triangle(v_0, v_1, v_2) = \mathrm{const.}

If you look at the terms in the edge functions containing px and py that shouldn’t be surprising: Summing the three terms for px gives (v0y – v1y + v1y – v2y + v2y – v0y) = 0, and similar for py. So yes, the sum of these three is constant alright. Now, looking at this in linear algebra terms, this shouldn’t come as a surprise: we have 3 affine functions on only 2 variables – they’re not going to be independent. But it still helps to see the underlying geometry.

Why signed areas are a good idea

Note that the statement about the edge functions summing up to the area of the triangle hold for any point, not just points inside the triangle. It’s not clear how that’s going to work when p is outside the triangle, so let’s have a look:

Triangle with p outside This time, the triangles actually overlap each other: The two triangles v0v1p and v1v2p are wound counter-clockwise and have positive area, same as before – also, they extend outside the area of the original triangle. But the third (red) triangle, v2v0p, is wound clockwise and has negative area, and happens to exactly cancel out the parts of the two other triangles that extend outside the original triangle v0v1v2. So it still all works out. If you haven’t seen this before, this kind of cancelling is an important trick, and can be used to simplify a lot of things that would otherwise be pretty hairy. For example, it can be used to calculate the area of any polygon, no matter how complicated, by just summing the areas of a bunch of triangles, one triangle for each edge. Doing the same using only positive-area triangles requires triangulating the polygon first, which is a much hairier problem, but again, I digress.

So where’s the barycentric coordinates already?

Now, this blog post is called “the barycentric conspiracy”, but strangely, this far in, we don’t seem to have seen a single barycentric coordinate yet. What’s up with that? Well, let’s first look at what barycentric coordinates are: in the context of a triangle, the barycentric coordinates of a point are a triple (w0, w1, w2) of numbers that act as “weights” for the corresponding vertices. So the three coordinate triples (1,0,0), (0,1,0) and (0,0,1) correspond to v0, v1 and v2, respectively. More generally, we allow the weights to be anything (except all zeros) and just divide through by their sum in the end. Then the barycentric coordinates for p are a triple (w0, w1, w2) such that:

\displaystyle p = \frac{w_0 v_0 + w_1 v_1 + w_2 v_2}{w_0 + w_1 + w_2}

Since we divide through by their sum, they’re only unique up to scale – much like the homogeneous coordinates you’re hopefully familiar with as a graphics programmer. This is the second time we’ve accidentally bumped into them in this post. That is not an accident. Barycentric coordinates are a type of homogeneous coordinates, and in fact both were introduced in the same paper by Möbius in 1827. I’m trying to stick with plain planar geometry in this post since it’s easier to draw (and also easier to follow if you’re not used to thinking in projective geometry). That means the whole homogeneous coordinate angle is fairly subdued in this post, but trust me when I say that everything we’ve been doing in here works just as well in projective spaces. And you’ve already seen the geometric derivations for everything, so we can even do it completely coordinate-free if we wanted to (always good to know how to avoid the algebra if you’re not feeling like it).

But back to barycentric coordinates: We already know that our edge functions measure (signed) areas, and that they’re zero on their respective edges. Well, both v0 and v1 are on the edge v0v1 (obviously), and hence

F_{01}(v_0) = F_{01}(v_1) = 0.

And we also already know that if we plug the third vertex into the edge function, we get twice the signed area of the whole triangle:

F_{01}(v_2) = 2 \triangle(v_0,v_1,v_2).

The same trick works with the other two edge functions: whenever all three vertices are involved, we get twice the signed area of the whole triangle, otherwise the result is zero. And we already know they’re affine functions. At this point, things should already look fairly suspicious, so I’m just gonna cut to the chase: Let’s set

w_0(p) := F_{12}(p)
w_1(p) := F_{20}(p)
w_2(p) := F_{01}(p)

That’s right, the three edge functions, evaluated at p, give us p’s barycentric coordinates, normalized so their sum is twice the area of the triangle. Note that the barycentric weight is always for the vertex opposite the edge we’re talking about. Now that you’ve seen the area diagram, it should be clear why: what the edge function F12(p) gives us is the scaled area of the triangle v1v2p, and the further p is from edge v1v2, the larger that triangle is. At the extreme, when p is at v0, it covers the entirety of the original triangle we started out with. So that all makes sense. While we’re at it, let’s also define a normalized version of the barycentric coordinates with their sum always being 1:

\lambda_0(p) := F_{12}(p) / 2 \triangle(v_0,v_1,v_2)
\lambda_1(p) := F_{20}(p) / 2 \triangle(v_0,v_1,v_2)
\lambda_2(p) := F_{01}(p) / 2 \triangle(v_0,v_1,v_2)

So the secret is out – the determinants we’ve been looking at, the signed areas and distances, even the edge functions – it was barycentric coordinates all along. It’s all connected, and everybody’s in on it! Cue scare chord.

Barycentric interpolation

And with that, we have all the math we need, but there’s one more application that I want to bring up: As I’ve said before, the barycentric coordinates are effectively weights for the various vertices. The definition uses this for the positions, but we can use those same weights to interpolate other stuff that’s supposed to vary linearly across a triangle, such as vertex attributes.

Now, for the depth buffer rasterizer that we’re going to look at, we only need to interpolate one thing, and that’s depth. If we have z values z0, z1, z2 at the vertices, we can determine the interpolated depth by computing

z(p) := \lambda_0(p) z_0 + \lambda_1(p) z_1 + \lambda_2(p) z_2

and if we have the edge function values for p already, that’s fairly straightforward and works just fine, at the cost of three multiplies and two adds. But remember that we have the whole thing normalized so the lambdas sum to 1. This means we can express any lambda in terms of the two others:

\lambda_0 + \lambda_1 + \lambda_2 = 1 \quad \Leftrightarrow \quad \lambda_0 = 1 - \lambda_1 - \lambda_2

Plugging this into the above expression and simplifying, we get:

z(p) = z_0 + \lambda_1(p) (z_1 - z_0) + \lambda_2(p) (z_2 - z_0)

The differences between the zi‘s are constant across the triangle, so we can compute them once. This gives us an alternative barycentric interpolation expression that uses two multiplies and two adds, in a form that allows them to be executed as two fused multiply-adds. Now if there’s one thing we’ve seen in the previous posts in this series, it’s that counting operations is often the wrong way to approach performance problems, but this one simplification we will end up using in an inner loop that’s actually bottlenecked by the number of instructions executed. And, just as importantly, this is also the expression that GPUs normally use for vertex attribute interpolation. I might talk more about that at some point, but there’s already more than enough material for one sitting in this post. So see you next time, when we learn how to turn all this into a rasterizer.

From → Maths

  1. marclo permalink

    Fantastic post. I have a question. You are talking about computing the barycentric coordinates for a 2D triangle where I assume point P in the case of a rasteriser would be in the point in the center of a given pixel. Now at this point the 2D triangle vertices have undergone the perspective divide. v0.x = v0cam.x / v0cam.z, etc. So are the barycentric coordinates perspective divided as well as this point. And if that’s the case, is it okay to actually use them to interpolate z itself?

    • “You are talking about computing the barycentric coordinates for a 2D triangle where I assume point P in the case of a rasteriser would be in the point in the center of a given pixel.”
      Or whichever other sample point you choose, yeah. It’s easy to support multisampling in this framework: the edge functions evaluated at different sample offsets all increase at exactly the same rates as you step across the pixel grid, meaning you don’t have to iterate them all individually; you can keep track of only one “master” edge function (usually the one for the center of the pixel or 2×2 quad), the edge functions for all other sample locations are always a constant offset from the master edge function that you can compute once during triangle setup.

      So are the barycentric coordinates perspective divided as well as this point. And if that’s the case, is it okay to actually use them to interpolate z itself?
      The barycentric coordinates computed using this technique are in screen space not clip space, i.e. not perspective corrected. So you can’t use them to interpolate texture coordinates, for example.

      But you *can* use them to interpolate Z. Note that what’s written into a Z-buffer is not view-space z (which gets moved into the “w” component of your clip-space coordinates by the usual perspective projection matrices), but what I (consistently throughout this series and my “A trip through the Graphics Pipeline” series) write as capital Z, which is post-projection: Z = v_clip.z / v_clip.w, where v_clip is the clip-space vertex position computed during vertex processing (what would be the output of your vertex shader).

      As said, the usual perspective projection matrices put view-space z into w (sometimes sign-flipped, depending on what coordinate conventions you use). What this means is that Z = v_clip.z / v_clip.w is a function of (1 / v_clip.w) = 1/v_view.z. Which means that Z varies linearly in screen space!

      This is why GPUs use Z-buffers: Z is easy to compute in screen-space and doesn’t need perspective correction. Which means you can do all Z-testing (and associated early rejection!) before ever doing the per-pixel perspective divide. (A while back, GPUs used to also support “W-buffering”, which writes clip-space W – i.e. view-space z – into the depth buffer. This is much more expensive since it does require a per-pixel divide, or at least a reciprocal. This was slow and never saw wide use as far as I can tell.)

      • marclo permalink

        Even better. Thank you so much. It’s was very helpful, was well as everything else on your blog. It would be great to have a little bit more code sometimes ;-) to make it easier for people like me with a poor math background to really understand how theory translates to the real world, but the content you provide is already hugely helpful. Many thx again.

      • sgm permalink

        “Or whichever other sample point you choose, yeah. It’s easy to support multisampling in this framework”
        Can this framework be extended to say a stochastic rasterizer (5D rasterization with N samples)? Would that be very inefficient?

      • This is the basis for most stochastic rasterization algorithms, yes. But once you distribute over time your edge functions (generally) stop being linear and you need a higher-order approximation.

  2. humanapp permalink

    Such a great blog series, even 9+ years later. Detailed, informative, and an easy read. But could you please expand your math in the section where you say “and if we rearrange terms a bit, regroup and simplify we get..”. I cannot follow the rearrangement! My algebra skills have rusted, I think.

    • Distribute the products. a*(b-c) = a*b – a*c. Apply to both products (the first is a=v_1x-v_0x, b=p_y, c=v_0y, second is a=v_1y-v_0y, b=p_x, c=v_0x. Finally -(v_1x – v_0x) * p_x = (v_0x – v_1x) * p_x (this is -(a-b) = (b-a)).

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  1. Triangle rasterization in practice « The ryg blog
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