CSE 270 (WI 2024) Discrete Differential Geometry

Welcome to CSE 270 "Discrete Differential Geometry"

  • Lecture: Tue Thu 2:00pm - 3:20pm
  • Discussion session: Wed 2:00pm - 2:50pm
  • Classroom: Warren Lecture Hall 2207.
  • Instructor: Albert Chern (Office Hour: Tue 3:30pm - 4:30, CSE building 4112)
    • TA: Hang "Hesper" Yin (Office Hour: Wed 3:30pm-4:30pm, CSE building 4217)
  • Sites:
  • Lecture note: Discrete Differential Geometry

Syllabus

Week Tuesday (Lecture) Wednesday (Discussion) Thursday (Lecture)
1 1/9: Introduction, Houdini 1/10
Houdini rendering tips
Möbius transform, digital asset
  • week1.zip
    		•  1/11: Triangle meshes, Euler characteristics
  • Slides (Meshes)
    		•
    2 1/16: Half edges, introduction to Laplacian
  • Slides (Mesh data structure)
  • Slides (Laplacian)
    		•  1/17
    Level sets on surfaces
    Area using hedge data struct
    Numpy slicing
  • week2.hipnc
    		•  1/18: Discrete Laplacian, Discrete exterior calculus (DEC)
    3 1/23: Discrete exterior calculus (DEC) 1/24
    Wave equation
    vector field visualization
    		 1/25: Vectors, covectors, differential
    4 1/30: Differential 1/31
    Boundary conditions on Poisson equation     		2/1: Exterior calculus
    5 2/6: Exterior calculus 2/7 2/8: Finite Element Exterior calculus
    6 2/13: Differential geometry of curves 2/14
    Curvature domain processing     		2/15: Differential geometry of curves
    7 2/20: Differential geometry of surfaces 2/21
    Minimal surface     		2/22: Differential geometry of surfaces
    8 2/27: Differential geometry of surfaces 2/28 2/29: Vector field design, gauge theory
    9 3/5: Vector field design, gauge theory 3/6
    Stripe patterns on surfaces stripe_pattern.hipnc     		3/7: Vector field design, Hodge decomposition
    10 3/12: Hodge decomposition 3/13
    No discussion     		3/14: Optimal transport
  • Slides (Optimal transport)
    		•
    Final 3/19: No class
    • HW4 due
    		 3/20: No class 3/21: No class
    Spring break

    Overview

    Discrete Differential Geometry introduces the mathematics and algorithms for digital geometry processing and simulation problems. Think of it as applied differential geometry for computer graphics and computational mathematics.

    What is discrete differential geometry? In the traditional approach for many computational and engineering problems, one (1) derives the governing equation with multivariable calculus on a coordinate system; (2) applies a generic discretization scheme (finite difference/finite element) to approximate the solution. This direction becomes extremely tedious as the domain of the problem becomes a more general shape (as often encountered in computer aided designs and computer graphics), where no natural coordinate system may fit the geometry featured in the problem. Also, discretization schemes based on approximation theory don't usually preserve the structures (e.g. conservation laws) of the underlying physical or geometric system.

    Discrete differential geometry takes a different route. Starting from the modeling stage (deriving governing equation) all the way to algorithmic stage, we use the language of geometry. It bypasses any artificial coordinate systems, and only speaks about relationships between points, lines, surfaces, and the field defined on top of them. During discretization, we directly find the discrete analogs of these relation, preserving as many differential geometry theorems as possible.

    Some highlights:

    • Houdini software: We will use the Houdini software for implementation. It is an industry standard in computer graphics. Most of the assignment in this course involves writing some shader code to manipulate the geometry, and occasionally Python code to solve linear equations.
    • Exterior Calculus: Exterior calculus is an approach to multivariable that is much more resilient to change of coordinates, making differential geometric problem formulation much more elegant.
    • Laplacian: You may have seen the Laplace operator in physics, image processing or other signal processing. It measures the second order derivative of a field. We will build a discrete Laplacian on a general triangulated surface. Many applications in geometry processing is based on solving a linear system with Laplacian.
    • Curves and Surfaces: Many simulation problems involve curves and surfaces. Given such a shape, what are the geometrically meaningful thing that we can measure?
    • Hodge Decomposition: Linear algebra of the differential operators. We can analyze the null space of these problems from the topology of the domain. An application of Hodge decomposition is fluid simulation.
    • Conformal flattening: Find a texture map from a given surface to the plane that preserves angles (no shearing distortion).
    • Vector field design: Find a smoothest tangent vector field on a given surface, and observe singularity nucleates at the optimal location on the surface. The underlying theory has its origin in quantum field and condensed matter physics.

    Course Logistics

    Getting Started (make sure to follow it in Week 1)

    1. Sign up to Piazza and Gradescope. 
    2. HW0 is due 1/18 (Thu). 

    Class Rules

    • Do the assignments individually. Discussion is strongly encouraged, since there are many challenging new mathematical concepts and part of the learning experience is to teach your classmates who didn't get it.
    • You can look up coding/math questions online. You are also welcomed to post questions (and answer others' questions) on Piazza.
    • Follow Academic Integrity (c.f. https://senate.ucsd.edu/Operating-Procedures/Senate-Manual/Appendices/2).

    Grades

    There is no quiz or exam. The grades are made of the following HWs:

    • HW0: 4%
    • HW1: 24%
    • HW2: 24%
    • HW3: 24%
    • HW4: 24%
    The final letter grade depends on the grade distribution.

    Late policy

    • HW0–4 late penalty: You have one quota of using a 24 hour extension.

    Homework