In this lecture we wrap up our discussion of discrete curvature, and see how it all fits together into a single unified picture that connects the integral viewpoint, the variational viewpoint, and the *Steiner formula*. Along the way we’ll touch upon several of the major players in discrete differential geometry, including a discrete version of Gauss-Bonnet, Schläfli’s polyhedral formula, and the cotan Laplace operator—which will be the focus of our next set of lectures.

# Category: Slides

## Lecture 15—Discrete Curvature I (Integral Viewpoint)

Just as curvature provides powerful ways to describe and analyze smooth surfaces, discrete curvatures provide a powerful way to encode and manipulate digital geometry—and is a fundamental component of many modern algorithms for surface processing. This first of two lectures on discrete curvature from the *integral* viewpoint, i.e., integrating smooth expressions for discrete curvatures in order to obtain curvature formulae suitable for discrete surfaces. In the next lecture, we will see a complementary *variational* viewpoint, where discrete curvatures arise by instead taking derivatives of discrete geometry. Amazingly enough, these two perspectives will fit together naturally into a unified picture that connects essentially all of the standard discrete curvatures for triangle meshes.

## Lecture 14—Smooth Curvature

Much of the geometry we encounter in everyday life (such as curves and surfaces sitting in space) is well-described by it *curvatures*. For instance, the fundamental theorem for plane curves says that an arc-length parameterized plane curve is determined by its curvature function, up to rigid motions. Similar statements can be made about surfaces and their curvatures, which we explore in this lecture.

## Lecture 13—Discrete Surfaces

We’ll follow up our lecture on smooth surfaces with a view of surfaces from the discrete point of view. Our goal will be to translate basic concepts (such as the differential, immersions, etc.) into a purely discrete language. Here we’ll also start to see the benefit of developing discrete differential forms: many of the statements we made about surfaces in the smooth setting can be translated into the discrete setting with minimal effort. As we move forward with discrete differential geometry, this “easy translation” will enable us to take advantage of deep insights from differential geometry to develop practical computational algorithms.

## Lecture 12—Smooth Surfaces

This lecture gives a crash course in the differential geometry of surfaces. There’s of course way more to know about surfaces than we can pack into a single lecture (and we’ll see plenty more later on), but this lecture will cover basic concepts like how to describe a surface and its normals. It also starts to connect surface theory to the other tools we’ve been building up, via *vector-valued* differential forms.

## Lecture 11—Discrete Curves

This lecture presents the discrete counterpart of the previous lecture on smooth curves. Here we also arrive at a discrete version of the fundamental theorem for plane curves: a discrete curve is completely determined by its discrete parameterization (a.k.a. edge lengths) and its discrete curvature (a.k.a. exterior angles). Can you come up with a discrete version of the fundamental theorem for space curves? If we think of torsion as the rate at which the binormal is changing, then a natural analogue might be to (i) associate a binormal \(B_i\) with each vertex, equal to the normal of the plane containing \(f_{i-1}\), \(f_{i}\), and \(f_{i+1}\), and (ii) associate a torsion \(\tau_{ij}\) to each edge \(ij\), equal to the angle between \(B_i\) and \(B_{i+1}\). Using this data, can you recover a discrete space curve from edge lengths \(\ell_{ij}\), exterior angles \(\kappa_i\) at vertices, and torsions \(\tau_{ij}\) associated with edges? What’s the actual algorithm? (If you find this problem intriguing, leave a comment in the notes! It’s not required for class credit.)

## Lecture 10—Smooth Curves

After spending a great deal of time understanding some basic algebraic and analytic tools (exterior algebra and exterior calculus), we’ll finally start talking about *geometry* in earnest, starting with smooth plane and space curves. Even low-dimensional geometry like curves reveal a lot of the phenomena that arise when studying curved manifolds in general. Our main result for this lecture is the *fundamental theorem of space curves*, which reveals that (loosely speaking) a curve is entirely determined by its curvatures. Descriptions of geometry in terms of “auxiliary” quantities such as curvature play an important role in computation, since different algorithms may be easier or harder to formulate depending on the quantities or variables used to represent the geometry. Next lecture, for instance, we’ll see some examples of algorithms for *curvature flow*, which naturally play well with representations based on curvature!

## Slides—Discrete Exterior Calculus

This lecture wraps up our discussion of discrete exterior calculus, which will provide the basis for many of the algorithms we’ll develop in this class. Here we’ll encounter the same operations as in the smooth setting (Hodge star, wedge product, exterior derivative, etc.), which in the discrete setting are encoded by simple matrices that translate problems involving differential forms into ordinary linear algebra problems.

## Slides—Discrete Differential Forms

In this lecture, we turn smooth differential \(k\)-forms into discrete objects that we can actually compute with. The basic idea is actually quite simple: to capture some information about a differential \(k\)-form, we integrate it over each oriented \(k\)-simplex of a mesh. The resulting values are just ordinary numbers that give us some sense of what the original \(k\)-form must have looked like.

## Slides—Exterior Calculus II: Integration

Our first lecture on exterior calculus covered differentiation; our second lecture completes the picture by discussing integration of differential forms. The relationship between integration and differentiation is encapsulated by *Stokes’ theorem*, which generalizes the fundamental theorem of calculus, as well as many other important theorems from vector calculus and complex analysis (divergence theorem, Green’s theorem, Cauchy’s integral formula, *etc.*). Stokes’ theorem also plays a key role in numerical discretization of geometric problems, appearing for instance in finite volume methods and boundary element methods; for us it will be the essential tool for developing a discrete version of differential forms that we can actually compute with.