The question is why do we want to have height maps instead of normal maps in the first place? Well, there are various reasons but the most obvious are that they are easier to edit, generally easier to understand and the result, when applied as displacement map, is easier to anticipate. In other words, while normals maps do have some advantages, they are generally less intuitive to work with and that’s probably a good reason why we might want to convert them. But before going into details how the conversion is done, let’s quickly recap what a normal map is and how it could be generated.
In fact it’s pretty easy and hence we’ll set up a simple example. Let’s take a grid, for example, and apply a noise function to it (in our case a mountainSop). Next compute point normals for the deformed grid and encode these normals as colors. Finally just write out the colors as texture map or directly generate the map by using the texture baker. And basically that’s it, the result is our normal map.
But now let’s reverse the process and assume we have our normal map but the geometry is just a flat grid. How can we reconstruct the original deformed geometry?
Well, let’s start simple. As we already know and the name “normal map” implies, normals are encoded as colors. To be more precise, the rgb values of our map correspond to the xyz components of the normals we are searching for. This means we can pretty much do a 1:1 conversion. The only thing we have to take care of is changing the range from 0, 1 (colors) to -1, 1 (normals). If we’ve done that we indeed end up with proper normals, the grid however, is still flat. What we need to do next is adjusting the points position/height so that the displaced grid finally matches the original deformed geometry. And that’s the tricky part. So, the question now is how are we going to get the height information out of our normals?
Well, one way for instance, is trying to solve this problem geometrically. This could be done by iteratively moving the points vertically up/down until new computed normals match the ones we got from our normal map. For a simple mesh/map this technique is fast and reasonably accurate but for more complicated maps and/or irregular meshes it starts failing and the result might just not be what we’d have expected. To overcome this problem there exist various other methods which could be used instead but my personal favourite is, once again, to rely on the famous Poisson equation.
If we assume height is a function on the mesh, it can be computed by minimizing the difference between our pre-computed normals (projected onto the tangent plane on the mesh) and the gradient of this function in the least squares sense. Since this is leading to the Poisson equation it’s easy to set up a linear system in the typical form A x = b. If we then use a fast sparse matrix library and let it do it’s magic by solving the system for x, we finally get what we were looking for, namely our desired height.
And of course, it works on non-flat meshes too.
Instead of setting up and solving everything in matrix form it’s also possible to do it with VEX/OpenCl although the setup is a bit different and it takes longer to solve. Anyways, for those who don’t have access to a fast matrix solver it’s a reasonable alternative. The process is pretty much the same as it’s done in this blog post.
I guess I know what you think: “Not again another post about vector fields!” Well, that might be true but vector fields are omnipresent in computer graphics and therefore it’s incredible important to get a basic understanding of vector calculus and how it’s used in Houdini. A vector field is, as the name implies, a field of vectors in n-dimensional space. In Houdini terms, it’s a bunch of vectors assigned to points, vertices, polys or voxels of a mesh or a volume. That’s roughly what a vector field is. It’s easy to understand and easy to visualize. However, much more interesting than what it is, is to know what we can do with it and how we compute it. And that’s exactly what I’m trying to explain in this blog post. I’m not going into all the details but trying to give a general overview in the context of Houdini’s Surface Operators, better known as SOPs.
So, how do we compute a vector field in Houdini? Well, honestly, there is no single answer to this question because it mainly depends on what kind of field we would like to generate. One of the most important vector fields, however, is the gradient vector field. So, what is a gradient vector field? The gradient is the first order derivative of a multivariate function and apart from divergence and curl one of the main differential operators used in vector calculus. In three-dimensional space we typically get it by computing the partial derivatives in x, y and z of a scalar function. In other words, if we apply the gradient operator on a scalar field we get a vector field. Well, that’s more or less the general definition but what does this mean in the context of Houdini? I’ll try to explain it on a simple example.
Lets create a grid, wire it into a mountainSop and we’ll get some kind of a landscape like in the images below. Now let’s have a look at different points on that “landscape” and measure their height. For every of these positions, the gradient is the direction in which the height (P.y) is changing the most. In other words, it is the direction of the steepest ascent. We could check this by simply sampling the height values of our landscape around the points. Just copy small circles onto the points, use a raySop to import the height attribute and search for the point with the largest value. This way we get a good approximation to the gradient, depending on the number of sampling points. Of course, that’s not how the gradient is computed but it might help to get a better understanding of it’s meaning. The height is simply the scalar input for the gradient operator and the result is a vector. If we apply the gradient operator on every point of the geometry, we finally get our gradient vector field over the mesh.
So far so good, but what’s so special about the gradient vector field you might ask. Well, a gradient field has several special and important properties and we are going to explore some of them by having a closer look at our previous example. For instance, if we generate contour lines of the scalar field (our height attribute which is the input for the gradient operator) we can see that the vectors are locally perpendicular to the isolines. If we rotate the vectors by 90 degrees, or rather compute the cross product with N, we get a vector field which is tangent to the contour lines. While both vector fields are tangent to the surface, the rotated vector field is in terms of its properties pretty much the opposite of the gradient. I won’t write much about it since I already did in another post, but nevertheless it’s important to mention what the main difference is. The gradient vector field is curl-free, it’s rotated counterpart, however, is a solenoidal vector field and hence divergence-free. If the field is curl- and divergence-free, it’s a laplacian (harmonic) vector field. But let’s go back to the gradient for now and have again a look at our “landscape” example.
Imagine, you’re standing on the top of the hill, put a ball on the ground and give it a light push in a specific direction. Most probably the ball will start rolling and if you’re not fast enough to stop it, it’s rolling down the hillside to the very bottom of the valley. Well, that’s nothing special and not much of a surprise. What is special, however, is the path the ball will take on its way down. It’s the path on which the height decreases locally as fast as possible and this route is exactly following the gradient vector field, or more precisely, the opposite direction in our example. This principle is called gradient descent/ascent and is used extensively for local minimization and maximization problems, as well as for local optimization problems in general. But mind, the keyword here is local. For global optimization problems we have to use different methods. This is quite obvious if we again look at our example. Depending on the sample point, or rather starting point of our path, we reach the local but not necessarily the global minimum/maximum. To make sure, that we actually find the global minimum, in other words, the point at the very bottom of the valley, we have to use a rather large number of samples.
Since the paths are following the gradient vector field, they are perpendicular to the contour lines of our height field and together they form a network of conjugate curves. Conjugate curve networks are quite important for many different applications such as remeshing, polygon planarization, parameterization and so on. The paths following the rotated vector field are, of course, parallel to the contour lines of our scalar function.
Talking about the gradient typically means that we have a scalar field, apply the gradient operator and finally get a vector field. The good thing about a gradient vector field is that we can reverse this operation. In other words, if we have a vector field which is a gradient field, we can calculate the original scalar field. This is possible as long as the vector field is curl-free, what a gradient field per definition is.
Understanding the concept of the gradient operator is quite important since it’s interrelated to pretty much everything in (discrete) differential geometry. For instance, if we apply the gradient operator on a scalar function, in other words, we compute the first order partial derivatives we get a vector as we already know. If we compute the second order partial derivative, the result is a matrix called the Hessian matrix. The trace of this matrix is then the Laplacian of the scalar function.
But now back to the beginning of the post. So, how do we compute the gradient? Well, the definition is quite simple: We have to compute the first order partial derivatives in x, y, and z. On volumes this is pretty easy to do since it’s basically a spatial grid and we can translate the equation pretty much one-to-one to VEX and use it in Houdini. On a parameterized surface we have to change it slightly and compute the derivatives in u and v. On meshes, however, Houdini’s prefered geometry type, we have to do it differently. The simplest method is just using the polyFrameSop in attribute gradient style. This way Houdini is doing the work for us and we don’t have to care about the underlying algorithm. If we don’t want to use the polyFrameSop for some reason, it’s fairly easy to implement our own version in VEX. If you take a look at the HIP you can find three examples. The first method uses the fact that the gradient vector field is perpendicular to contour lines. The second method is basically based on a piecewise linear function and the third is using point clouds to compute the gradient.
And finally some examples of using the gradient operator for various applications, such as magnetic field line generation, flow field generation, medial axis extraction and so on.
Some time ago I wrote a blog post about generating tangent and smoothly varying vector fields on arbitrary meshes. The method I’ve used works basically by specifying some constraints such as vortices, sources, sinks and guiding curves/edges, which are then interpolated over the surface to compute the final field. Depending on the constraints we achieve very different kinds of vector fields which have different properties and hence different applications. So, while the resulting vector field might be exactly what you need as velocity field in a fluid simulation, it might be completely useless for remeshing. The main reason for this is related to curl and/or divergence. Generally speaking, whenever we are computing or just working with vector fields, we always have to deal with at least one of the three interrelated main differential operators – gradient, divergence and curl.
For instance, applying the gradient operator on a scalar field results in a curl-free vector field. If we then apply a local rotation around 90 degrees, we get the orthogonal counterpart in the form of a divergence-free field. This is e.g. exactly what we need as velocity field for a fluid simulation since we usually want fluids to be incompressible. Furthermore we have to take care of open edges at which we usually want to have tangential boundary conditions, at least in case we are working with fluids.
Well, though all this sounds easy, the problem is that in most cases we just don’t have these nice velocity fields. Normally they are neither gradient fields nor solenoidal fields but rather a mixture of both. In other words we are dealing with vector fields which aren’t completely divergence-free. That’s the reason why we need to apply pressure projection in DOPS which finally computes a proper velocity field needed for fluids. This works nicely in the simulation environment on volumes. But what if we need to do the same in SOPS and on surfaces? Of course we could still rely on volumes, do all the operations we need to do and bring the result back onto the original mesh. While this works to some degree, it is far from being optimal – the result will most probably be inaccurate and we will be limited to solenoidal and gradient vector fields. What we could do instead is to work directly with vector fields on the surface. Typically any vector field on a simply-connected domain could be decomposed into the sum of an irrotational (curl-free), a solenoidal (divergence-free) and a harmonic (divergence-free and curl-free) field. This technique is known as Hodge-Helmholtz decomposition and is basically achieved by minimizing the energy functionals for the irrotational and the solenoidal component of the field by solving the Poisson equation. Finally the harmonic component will be computed as the residual.
While divergence-free vector fields are essential for any kind of fluid simulation, curl-free and harmonic fields are essential for many other applications, such as remeshing, texture synthesis and parameterization to name a few. The irrotational component of the vector field is curl-free and hence the field is integrable. It is equivalent to the gradient of a scalar potential function on simply-connected domains. This means that we can reproduce the scalar field from the irrotational vector field because it is exactly the gradient of the desired scalar field. The isolines of the scalar field are therefore exactly orthogonal to the vector field. In case the vector field is not curl-free, we can treat it as a gradient of a function and minimize the difference between the gradient and the given vector field in the least squares sense. Again this leads to the well known Poisson equation.