**Rasterization: What Are We Trying to Solve?**

In the previous chapter, we learned how to performed the first step of the rasterization algorithm in a way, which is to project the triangle from 3D space onto the canvas. This definition is not entirely accurate in fact, since what we actually really did was to transform the triangle from camera space to screen space, which as mentioned in the previous chapter, is also a three-dimensional space. However the x- and y-coordinates of the vertices in screen-space correspond to the position of the triangle vertices on the canvas, and by converting them from screen-space to NDC space and then finally from NDC-space to raster-space, what we actually get in the end are the vertices 2D coordinates in raster space. Finally, we also know that the z-coordinates of the vertices in screen-space holds the original z-coordinate of the vertices in camera space (inverted so that we deal with positive numbers rather than negatives ones).【上一章节讲的是将三角形从三维空间投影到canvas，最终得到裁剪空间的2D坐标和z值】

What we need to do next, is to loop over the pixel in the image and find out if any of these pixels overlap the “projected image of the triangle” (figure 1). In graphics APIs specifications, this test is sometimes called the **inside-outside test or the coverage test**. If they do, we then set the pixel in the image to the triangle’s color. The idea is simple but of course, we now need to come up with a method to find if a given pixel overlaps a triangle. This is essentially what we will study in this chapter. We will learn about the method that is typically used in rasterization to solve this problem. It uses a technique known as the** edge function** which we are now going to describe and study. This edge function is also going to provide valuable information about the position of the pixel within the projected image of the triangle known as barycentric coordinates. **Barycentric coordinates** play an essential role in computing the actual depth (or the z-coordinate) of the point on the surface of the triangle that the pixel overlaps. We will also explain what barycentric coordinates are in this chapter and how they are computed.【下一步我们要做的是递归所有的overlap pixel。图形API中叫这一步叫做 inside-outside test，这边方法的主要思想第一章讲过，主要要注意的是边界的处理，这边使用的技术叫edge function来处理】

At the end of this chapter, you will be able to actually produce a very basic rasterizer. In the next chapter, we will actually look into the possible issues with this very naive implementation of the rasterization algorithm. We will list what these issues are as well as study how they are typically addressed.【这边还会给出基本的实现，以及一些常见问题的解决】

A lot of research has been done to optimize the algorithm. The goal of this lesson is not to teach you how to write or develop an optimized and efficient renderer based on the rasterization algorithm. The goal of this lesson is to teach the basic principles of the rendering technique. Don’t think though that the techniques we present in these chapters, are not actually used. They are used to some extent, but how they are actually implemented either on the GPU or in a CPU version of a production renderer, is just likely to be a highly optimized version of the same idea. What is truly important is to understand the principle and how it works in general. From there, you can study on your own about the different techniques which are used to speed up the algorithm. But the techniques presented in this lesson are generic and make up the foundations of any rasterizer.【有很多研究都是在优化这一步，但是本教材主要告诉你的是基本的方法，你可以自学其他的优化方法】

【牢记】

Keep in mind that drawing a triangle (since triangle is primitive we will use in this case), is a two steps problem:

- We first need to find which pixels overlap the triangle.【找出三角形overlap哪些pixel】
- We then need to define which colors should the pixels overlapping the triangle be set to, a process that is called shading【决定pixel的颜色】

**The Edge Function**

As mentioned above, they are several possible methods to find if a pixel overlaps a triangle. It would be good to document older techniques, but in this lesson, will only present the method that is generally used today. This method was presented by Juan Pineda in 1988 and a paper called “**A Parallel Algorithm for Polygon Rasterization**” (see references in the last chapter).【这里我们所采用的方法】

Before we look into Pineda’s technique itself, we will first describe the principle of his method. Let’s say that the edge of a triangle can be seen as a line splitting the 2D plane (the plane of the image) in two (as shown in figure 2). The principle of Pineda’s method is to find a function which he called the edge function, so that when we test on which side of this line a given point is (the point P in figure 2), the function returns a negative number when it is to the left of the line, a positive number when it is to the right of this line, and zero, when the point is exactly on the line.【每一条边可以看作是划分plane成两部分的线，这个方法就是找出edge functions，然后测试point在这些线的哪一边，坐便输出positive number，右边输出 negative number。如下图所示】

In figure 2, we applied this method to the first edge of the triangle (defined by the vertices v0-v1. Be careful the order is important). If we now apply the same method to the two other edges (v1-v2 and v2-v0), we then can clearly see that there is an area (the white triangle) within which all points are positive (figure 3). If we take a point within this area, then we will find that this point is to the right of all three edges of the triangle. If P is in fact a point in the centre of a pixel, we can then use this method to find if the pixel overlaps the triangle. If for this point, we find that the edge function returns a positive number for all three edges, then the pixel is contained in the triangle (or may lie on one of its edges). The function Pinada uses also happens to be linear which means that it can be computed incrementally but we will come back on this point later.【上图只考虑一条边，这里考虑三条边如下图。顺时针描述线，则point的描述结果都为positive的话，就是在三角形内部】

Now that we understand the principle, let’s find out what that function is. The edge function is defined as (for the edge defined by vertices V0 and V1):【来看公式表述】

As the paper mentions, this function has the useful property that its value is related to the position of the point (x,y) relative to the edge defined by the points V0 and V1:【得到的结果与意义如下】

- E(P) > 0 if P is to the “right” side
- E(P) = 0 if P is exactly on the line
- E(P) < 0 if P is to the “left ” side

In fact this function is equivalent in mathematics to the magnitude of the cross products between the vector (v1-v0) and (P-v0). We can also write these vectors in a matrix form (presenting this as a matrix has no other interest than just presenting the two vectors in a neat way):【三条边一起考虑，可以矩阵表示】

If we write that **A=(P−V0)A=(P−V0)** and **B=(V1−V0)B=(V1−V0)**, then we can also write the vectors A and B as a 2×2 matrix:

The determinant of this matrix can be computed as:

【这就是矩阵表示的最终公式】

Understanding what’s actually happening is easier when we look at the result of a cross product between two 3D vectors (figure 4). 【为了理解，我们来看图四】

In 3D, the cross-product returns another 3D vector which is perpendicular (or orthonormal) to the two original vectors. But as you can see in figure 4, the magnitude of that orthonormal vector also changes with the orientation of the two vectors with respect to each other. In figure 4, we assume a right-hand coordinate system. When the two vectors A (red) and B (blue) are either pointing exactly in the same direction or in opposite directions, the magnitude of the third vector C (in green) is zero. Vector A has coordinates (1,0,0) and is fixed. When vector B has coordinates (0,0,-1), then the green vector, vector C has coordinate (0,-1,0). If we were to find its “signed” magnitude, we would find that it is equal to -1. On the other hand, when vector B has coordinates (0,0,1), then C has coordinates (0,1,0) and its signed magnitude is equal to 1. In one case the “signed” magnitude is negative, and in the second case, the signed magnitude is positive. In fact, in 3D, the magnitude of a vector can be interpreted as the area of the parallelogram having A and B as sides as shown in figure 5 (read the Wikipedia article on the cross product to get more details on this interpretation):【这边想说的是上面的公式可以看作是坐标系变换，把ABC看作是一个坐标系，就可以标出一个positive空间】

Obviously an area should always be positive, though the sign of the above equation provides an indication about the orientation of the vectors A and B with respect to each other. When with respect to A, B is within the half plane defined by vector A and a vector orthogonal to A (let’s call this vector D; note that A and D form an 2D Cartesian coordinate system), then the result of the equation is positive. When B is within the opposite half plane, the result of the equation is negative (figure 6). Another way of explaining this result, is that the result is positive when the angle θθ is in the range ]0,π[]0,π[ and negative when θθ is in the range ]π,2π[]π,2π[. Note then when theta is exactly equals to 0 or ππ then the cross-product or the edge function returns 0.【上面公式的另一解释，半平面方面的理解（妈的一点也不直观，没看明白这东西怎么表示，不看没关系）】

To find if a point is inside a triangle, all we care about really is the sign of the function we used to compute the area of the parallelogram. However, the area itself also plays an important role in the rasterization algorithm; it is used to compute the barycentric coordinates of the point in the triangle, a technique we will study next. The cross product in 3D and 2D has the same geometric interpretation, thus the cross-product between two 2D vectors also returns the “signed” area of the parallelogram defined by the two vectors. The only difference is that in 3D, to compute the area of the parallelogram you need to use this equation:【为了判断point是否在三角形内，我们关心的是公式解的符号。但是其实三角形内部区域本身也很重要，它用来计算三角形中点的重心坐标，3D计算公式如下】

while in 2D, this area is given by the cross-product itself (which as mentioned before can also be interpreted as the determinant of a 2×2 matrix):【2D计算公式】

From a practical point of view, all we need to do now, is test the sign of the edge function computed for each edge of the triangle and another vector defined by a point and the first vertex of the edge (figure 7).【实际实现就是对于每个点检测三条边公式如下】

If all three tests are positive or equal to 0, then the point is inside the triangle (or lie on one of the edges of the triangle). If any one of the test is negative, then the point is outside the triangle. In code we get:【如果结果都是positive则是在三角形内】

**Alternative to the Edge Function**

There are obviously other ways than the edge function method to find if pixels overlap triangles, however as mentioned in the introduction of this chapter, we won’t study them in this lesson. Just for reference though, the other common technique is called scanline rasterization. It is based on the Brenseham algorithm that is generally used to draw lines. GPUs use the edge method mostly because it is more generic than the scanline approach which is also more difficult to run in parallel that the edge method, but we won’t provide more information on this topic in this lesson.【其他替代方法自己看去，这里有reference】

**Be Careful! Winding Order Matters**

One of the things we have been talking about yet, but which has a great importance in CG, is the order in which you declare the vertices making up the triangles. They are two possible conventions which you can see illustrated n the figure 8: clockwise or counter-clockwise ordering or winding. Winding is important because it essentially defines one important property of the triangle which is the orientation of its normal. Remember that the normal of the triangle can be computed from the cross product of the two vectors A=(V2-V0) and B=(V1-V0). Let’s say that V0={0,0,0}, V1={1,0,0} and V2={0,-1,0} then (V1-V0)={1,0,0} and (V2-V0)={0,-1,0}. Let’s now compute the cross product of these two vectors:【这个算法非常重要的一点是要注意三角形顶点顺序，顺时针和逆时针会得到相反的结果】

As expected, the two normals are pointing in opposite directions. The orientation of the normal has a great importance for lots of different reasons, but one of the most important ones is called **face culling**. Most rasterizers and even ray-tracer for that matter may not render triangles whose normal is facing away from the camera. This is called back-face culling. Most rendering APIs such as OpenGL or DirectX give the option to turn back-face culling off, however you should still be aware that vertex ordering plays a role in what’s actually rendered, among many other things. And not surprisingly, the edge function is one of these other things. Before we get to explaining why it matters in our particular case, let’s say that there is not particular rule when it comes to choosing the order. In reality so many details in a renderer implementation may change the orientation of the normal that you can’t assume that by declaring vertices in a certain order, you will get the guarantee that the normal will be oriented a certain way. For instance rather that using the vectors (V1-V0) and (V2-V0) in the cross-product you could as have used (V0-V1) and (V2-V1) instead. It would have produced the same normal but flipped. Even if you use the vectors (V1-V0) and (V2-V0), remember that the order of the vectors in the cross-product changes the sign of the normal: A×B=−B×AA×B=−B×A. So the direction of your normal also depends of the order of the vectors in the cross-product. For all these reasons, don’t try to assume that declaring vertices in one order rather than the other will give you one result or the other. What’s important though, is that once you stick to the convention you have chosen. Generally, graphics APIs such as OpenGL and DirectX expect triangles to be declared in counter-clockwise order. We will also use counter-clockwise winding. Now let’s see how ordering impacts the edge function.【不同的顶点排列顺序得到的法线结果不一样，法线也很重要，正好可以用于背面剔除，OpenGL/DX都是这么用的，顺便更进一步就是用来做光栅化。】

**Barycentric Coordinates (重心坐标)**

Computing barycentric coordinates is not necessary to get the rasterization algorithm working. For a really naive implementation of the rendering technique, all you need is to project the vertices and use a technique like the edge function that we described above, to find if pixels are inside triangles. These are the only two necessary steps to produce an image. However the result of the edge function which as we explained above, can be interpreted as the area of the parallelogram defined by vector A and B can actually directly be used to compute these barycentric coordinates. Thus, it makes sense to study the edge function and the barycentric coordinates at the same time.【计算重心坐标在光栅化这一步不是必要的，但是可以在这一步顺便计算出来给其他方面使用】

Before we get any further though, let’s explain what these barycentric coordinates are. First, they come in a set of three floating point numbers which in this lesson, we will denote λ0λ0, λ1λ1 and λ2λ2. Many different conventions exist but Wikipedia uses the greek letter lambda as well (λλ) which is also used by other authors (the greek letter omega ωω is also sometimes used). This doesn’t matter, you can call them the way you want. In short, the coordinates can be used to define any point on the triangle in the following manner:【如果我们有三个点，则任意的点P可以表示为如下公示所示】

Where as usual, V0, V1 and V2 are the vertices of a triangle. These coordinates can take on any value, but for points which are inside the triangle (or lying on one of its edges) they can only be in the range [0,1] and the sum of the three coordinates is equal to 1. In other words:【如果三个点是三角形顶点，则P如果在三角形内，lamda的值都应该在[0,1]内且相加为1，如下所示】

This is a form of interpolation if you want. They are also sometimes defined as weights for the triangle’s vertices (which is why in the code we will denote them with the letter w). **A point overlapping the triangle can be defined as “a little bit of V0 plus a little bit of V1 plus a little bit of V2”**. Note that when any of the coordinates is 1 (which means that the others in this case are necessarily 0) then the point P is equal to one of the triangle’s vertices. For instance if λ2=1λ2=1 then P is equal to V2. Interpolating the triangle’s vertices to find the position of a point inside the triangle is not that useful. But the method can also be used to interpolate across the surface of the triangle any quantity or variable that has been defined at the triangle’s vertices. Imagine for instance that you have defined a color at each vertex of the triangle. Say V0 is red, V1 is green and V2 is blue (figure 12). What you want to do, is find how are these three colors interpolated across the surface of the triangle. If you know the barycentric coordinates of a point P on the triangle, then its color CPCP (which is a combination of the triangle vertices’ colors) is defined as:【这就是我们所期望的差值表示形式，这个差值可以直接用于颜色的平滑表示，如图12，点p的颜色结果就直接可以差值得到，公式如下】

This is a very handy technique which is going to be useful to shade triangles. Data associate with the vertices of triangles are called vertex attribute. This is a very common and very important technique in CG. The most common vertex attributes are colors, normals and texture coordinates. What this means in practice, is that generally when you define a triangle you don’t only pass on to the renderer the triangle vertices but also its associated vertex attributes. For example if you want to shade the triangle you may need a color and normal vertex attribute, which means that each triangle will be defined by 3 points (the triangle vertex positions), 3 colors (the color of the triangle vertices) and 3 normals (the normal of the triangle vertices). Normals too can be interpolated across the surface of the triangle. Interpolated normals are used in a technique called **smooth shading** which was first introduced by Henri Gouraud. We will explain this technique later when we get to shading.【smooth shading就是这么实现的】

【原文这里还描述了面积与lamda之间的关系的推导，目测不是特别有用不再赘述，这里的结论还是需要的，就是告诉你怎么去求解lamda的值，lamda就是各个面积的比值结果，也就是E对应的比值结果】

Let’s see how it looks in code. We were already computing the edge functions before to test if points were inside triangles. Only, in our previous implementation we were just returning true or false depending on whether the result of the function was either positive or negative. To compute the barycentric coordinates, we need the actual result of the edge function. We can also use the edge function to compute the area (multiplied by 2) of the triangle. Here is a version of an implementation that tests if a point P is inside a triangle and if so, computes its barycentric coordinates:【代码，edge function的实现】

**Interpolate vs. Extrapolate**

One thing worth noticing is that the computation of barycentric coordinates works independently from its position with respect to the triangle. In other words, the coordinates are valid if the point is inside our outside the triangle. When the point is inside, using the barycentric coordinates to evaluate the value of a vertex attribute is called **interpolation**, and when the point is outside, we speak of **extrapolation**. This an important detail because in some cases, we will have to evaluate the value of a given vertex attribute for points that potentially don’t overlap triangles. To be more specific, this will be needed to compute the derivatives of the triangle texture coordinates for example. These derivatives are used to filter textures properly. If you are interested in learning more about this particular topic we invite you to read the lesson on Texture Mapping. In the meantime, all you need to remember is that barycentric coordinates are valid even when the point doesn’t cover the triangle. You also need to know about the difference between vertex attribute extrapolation and interpolation.【这里值得注意的是点p用三个顶点表示的方式，不管p在不在三角形内，都是成立的。在三角形内的时候叫做差值，在三角形外的时候叫做外推，这个外推在texture坐标对应的时候还是很有用的】

**Rasterization Rules**

In some special cases, a pixel may overlap more than one triangle. This happens when a pixel lies exactly on an edge shared by two triangles as shown in figure 17. Such pixel would pass the coverage test for both triangles. If they are semi-transparent, a dark edge may appear where the pixels overlap the two triangles as a result of the way semi-transparent objects are combined with each other (imagine two super-imposed semi-transparent sheets of plastic. The surface is more opaque and looks darker than the individual sheets). You would get something similar to what you can see in figure 18, which is a darker line where the two triangles share an edge.【考虑一个特殊情况，如图所示，两个三角形的一条边共线，这时候如果三角形都是半透明的，光栅化的结果会是中间有一条比较深的线，原因是这里两个三角形都绘制了】

The solution to this problem is to come up with some sort of rule that guarantees that a pixel can never overlap twice two triangles sharing an edge. How do we do that? Most graphics APIs such as OpenGL and DirectX define something which they call the top-left rule. We already know the coverage test returns true if a point is either inside the triangle or if it lies on any of the triangle edges. What the top-left rule says though, is that the pixel or point is considered to overlap a triangle if it is either inside the triangle or lies on either a triangle top edge or any edge that is considered to be a left edge. What is a top and left edge? If you look at figure 19, you can easily see what we mean by top and left edge.【解决方法是需要有一个排序做法排除这种情况。Opengl/DirectX 采用的做法叫做 top-left rule，就是overlap的边界如果处在左边和上面的边会绘制，判断方法如下】

- A top edge is a edge that is perfectly horizontal and whose defining vertices are above the third one. Technically this means that the y-coordinates of the vector
**V[(X+1)%3]-V[X]**is equal to 0 and that its x-coordinates is positive (greater than 0). - A left edge is essentially an edge that is going up. Keep in mind that in our case, vertices are defined in clockwise order. An edge is considered to go up if its respective vector V[(X+1)%3]-V[X] (where X can either be 0, 1, 2) has a positive y-coordinate.

**Putting Things Together: Finding if a Pixel Overlaps a Triangle**

Let’s test the different techniques we learned about in this chapter, in a program that produces an actual image. We will just assume that we have projected the triangle already (check the last chapter of this lesson for a complete implementation of the rasterization algorithm). We will also assign a color to each vertex of the triangle. Here is how the image is formed. We will loop over all the pixels in the image and test if they overlap the triangle using the edge function method. All three edges of the triangle are tested against the current position of the pixel, and if the edge function returns a positive number for all the edges then the pixel overlaps the triangle. We can then compute the pixel’s barycentric coordinates and use these coordinates to shade the pixel by interpolating the color defined at each vertex of the triangle. The result of the frame-buffer is saved to a PPM file (that you can read with Photoshop). The output of the program is shown in figure 20.【到此为止可以完成光栅化这一步了，可以使用颜色填充三角形，可以解决多个三角形overlap的问题，可以输出如图所示的结果】

**Conclusion and What’s Next?**

There are many interesting techniques and trivia related to the topics barycentric coordinates but this lesson is just an introduction to the rasterization algorithm thus we won’t go any further. One trivia that is interesting to know though, is that barycentric coordinates are constant along lines parallel to an edge (as shown in figure 21).【同样还有很多相关的技术来解决同样的问题，这里只讲基础的，其他的自己看】

【总结】

In this lesson, we learned two important method and various concepts.

- First, we learned about the edge function and how it can be used to find if a point P overlaps a triangle. The edge function is computed for each edge of the triangle, and a second vector defined by the edge first vertex and another point P. If for all three edges, the function is positive, then point P overlaps the triangle.【使用edge function解决pixel是否在三角形内的问题】
- Furthermore, we also learned that the result of the edge function can also be used to compute the barycentric coordinates of point P. These coordinates can be used to interpolate vertex data or vertex attribute across the surface of the triangle. They can be interpreted as weights for the various vertices. The most common vertex attribute are color, normal and texture coordinates.【edge function也可以用来计算中心坐标等，作用于颜色，法线和纹理的处理】