CS 15-458/858: Discrete Differential Geometry
CARNEGIE MELLON UNIVERSITY | SPRING 2021 | TUE/THU 12:20-1:40 | Remote
In completing your assignment for finals next week, we thought you might find a couple videos helpful (though totally optional). The first was already linked to in the assignment writeup, which gives a reasonably short (18 minutes) motivation for and description of the algorithm you’ll be implementing. The second is a longer (50 minute) talk that covers the algorithm in more detail, and describes some fun extensions and applications of the same basic technique:
Enjoy!
Our final lectures for the term focus on geodesics, which generalize the notion of “straight line” to curved spaces; this material also connects to your final assignment, on computing geodesic distance. Once again we’ll play “The Game” of discrete differential geometry, and see how two natural characterizations from the smooth setting (straightest and locally shortest) lead to two distinct, and fascinating definitions in the discrete setting. Along the way we’ll also encounter many rich topics from (discrete) differential geometry including the cut locus, the medial axis, the exponential/log map, the covariant derivative, and the Lie bracket! We’ll also see how all this stuff connects with practical algorithms for things like surface reconstruction from points, and give two different algorithms for tracing out geodesics on curved surfaces.
Note that the lecture video comprises about two lectures (about two hours total). I’d recommend watching it in two logical chunks:
A basic task in geometric algorithms is finding mappings between surfaces, which can be used to transfer data from one place to another. A particularly nice class of algorithms (both from a mathematical and computational perspective) are conformal mappings, which preserve angles between vectors, and are generally very well-behaved. In this lecture we’ll take a look at smooth characterizations of conformal maps, which will ultimately inspire the way we talk about conformal maps in the discrete/computational setting.
The video covering both today and Thursday’s lecture (on discrete aspects of conformal maps) can be found here.
In this lecture we take a close look at the Laplacian, and its generalization to curved spaces via the Laplace-Beltrami operator. The Laplacian is one of the most fundamental objects in geometry and physics, and which plays a major role in algorithms. Here we consider several perspectives to build up some basic intuition about what the Laplacian is, and what it means.
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.
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.
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.
In this lecture we continue our discussion of smooth surfaces, introducing some key concepts like the Gauss map and the area vector. We’ll also sketch out how to finally talk about differential forms on curved surfaces, rather than in flat \(\mathbb{R}^n\).
This lecture takes a first look at smooth 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 two very important perspectives: the extrinsic description of a surface via a local parameterization, which tells us where points sit in space, and the intrinsic description of a surface via coordinate charts, which lets us work with a surface without worrying how it’s embedded in space.
This lecture presents the discrete counterpart of the previous lecture on smooth curves. Here we 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). We’ll also cook up a discrete version of the fundamental theorem for space curves, and give a bunch of neat examples of discrete curves in geometry processing and physical simulation.