liveBook · Manning

What you will learn in Geometry for Programmers

by Oleksandr Kaleniuk

Chapter by chapter

Chapter 1: Getting started

1.1 Which parts of programming require geometry? · 1.2 What is geometry for programmers? · 1.3 Why not let my tools take care of geometry? · 1.4 Applied geometry has been around forever; why learn it now? · 1.5 You don’t have to know much to start · 1.6 SymPy will do your math for you · 1.7 Summary

What you learn in chapter 1 →

Chapter 2: Terminology and jargon

2.1 Numbers, points, and vectors · 2.1.1 Numbers · 2.1.2 Vectors · 2.1.3 Section 2.1 summary · 2.2 Vertices and triangles · 2.2.1 Being pedantic about triangles · 2.2.2 Triangle quality · 2.2.3 Section 2.2 summary · 2.3 Lines, planes, and their equations · 2.3.1 Lines and planes · 2.3.2 The parametric form for 3D lines · 2.3.3 Section 2.3 summary · 2.4 Functions and geometric transformations · 2.4.1 What is a function? · 2.4.2 Function types most commonly used in geometric modeling · 2.4.3 Section 2.4 summary · 2.5 The shortest possible introduction to matrix algebra · 2.5.1 What is algebra? · 2.5.2 How does matrix algebra work? · 2.5.3 Section 2.5 summary · 2.6 Exercises · 2.7 Solutions to exercises · 2.8 Summary

What you learn in chapter 2 →

Chapter 3: The geometry of linear equations

3.1 Linear equations as lines and planes · 3.1.1 Introducing a hyperplane · 3.1.2 A solution is where hyperplanes intersect · 3.1.3 Section 3.1 summary · 3.2 Overspecified and underspecified systems · 3.2.1 Overspecified systems · 3.2.2 Underspecified systems · 3.2.3 Section 3.2 summary · 3.3 A visual example of an interactive linear solver · 3.3.1 The basic principle of iteration · 3.3.2 Starting point and exit conditions · 3.3.3 Convergence and stability · 3.3.4 Section 3.3 summary · 3.4 Direct solver · 3.4.1 Turning an iterative solver into a direct one · 3.4.2 Algorithm complexity · 3.4.3 Section 3.4 summary · 3.5 Linear equations system as matrix multiplication · 3.5.1 Matrix equations · 3.5.2 What types of matrices we should know about · 3.5.3 Things we’re allowed to do with equations · 3.5.4 Section 3.5 summary · 3.6 Solving linear systems with Gaussian elimination and LU-decomposition · 3.6.1 An example of Gaussian elimination · 3.6.2 What do “elimination” and “decomposition” mean? · 3.6.3 Section 3.6 summary · 3.7 Which solver fits my problem best? · 3.7.1 When to use an elimination-based one · 3.7.2 When to use an iterative one · 3.7.3 When to use neither · 3.7.4 Section 3.7 summary · 3.8 Practical example: Does a ray hit a triangle? · 3.8.1 The ray-triangle intersection problem · 3.8.2 Forming a system · 3.8.3 Making the equations into code · 3.8.4 Section 3.8 summary · 3.9 Exercises · 3.10 Solutions to exercises · 3.11 Summary

What you learn in chapter 3 →

Chapter 4: Projective geometric transformations

4.1 Some special cases of geometric transformations · 4.1.1 Translation · 4.1.2 Scaling · 4.1.3 Rotation · 4.1.4 Section 4.1 summary · 4.2 Generalizations · 4.2.1 Linear transformations in Euclidean space · 4.2.2 Bundling rotation, scaling, and translation in a single affine transformation · 4.2.3 Generalizing affine transformations to projective transformations · 4.2.4 An alternative to projective transformations · 4.2.5 Section 4.2 summary · 4.3 Projective space and homogeneous coordinates · 4.3.1 Expanding the whole space with homogeneous coordinates · 4.3.2 Making all the transformations a single matrix multiplication: Why? · 4.3.3 Section 4.3 summary · 4.4 Practical examples · 4.4.1 Scanning with a phone · 4.4.2 Does a point belong to a triangle? · 4.4.3 Section 4.4 summary · 4.5 Exercises · 4.6 Solutions to exercises · 4.7 Summary

What you learn in chapter 4 →

Chapter 5: The geometry of calculus

5.1 What is a derivative? · 5.1.1 Derivative at a point · 5.1.2 Derivative as a function · 5.1.3 Rules of differentiation · 5.1.4 Using SymPy to do differentiation · 5.1.5 Section 5.1 summary · 5.2 Smooth piecewise parametric curves · 5.2.1 Piecewise functions · 5.2.2 Smooth parametric curves · 5.2.3 Curvature · 5.2.4 Section 5.2 summary · 5.3 Practical example: Crafting a curve out of lines and circles · 5.3.1 The biarc building block · 5.3.2 The line segment and arc building block · 5.3.3 The combination of both · 5.3.4 Section 5.3 summary · 5.4 Exercises · 5.5 Solutions to exercises · 5.6 Summary

What you learn in chapter 5 →

Chapter 6: Polynomial approximation and interpolation

6.1 What are polynomials? · 6.1.1 Axis intersections and roots of polynomial equations · 6.1.2 Polynomial derivatives · 6.1.3 Section 6.1 summary · 6.2 Polynomial approximation · 6.2.1 Maclaurin and Taylor series · 6.2.2 The method of least squares · 6.2.3 Practical example: Showing a trend with approximation · 6.2.4 Section 6.2 summary · 6.3 Polynomial interpolation · 6.3.1 Using Vandermonde matrix to get the interpolating polynomial · 6.3.2 What limits polynomial interpolation application to small data only? · 6.3.3 How to lessen unwanted oscillations · 6.3.4 Lagrange interpolation: Simple, genius, unnecessary · 6.3.5 Practical example: Showing the trend with interpolation · 6.3.6 Section 6.3 summary · 6.4 Practical example: Showing a trend with both approximation and interpolation · 6.4.1 The problem · 6.4.2 The solution · 6.4.3 Section 6.4 summary · 6.5 Exercises · 6.6 Solutions to exercises · 6.7 Summary

What you learn in chapter 6 →

Chapter 7: Splines

7.1 Going beyond the interpolation · 7.1.1 Making polynomial graphs with tangent constraints · 7.1.2 Practical example: Approximating the sine function for a space simulator game · 7.1.3 Unexpected fact: Explicit polynomial modeling generalizes power series · 7.1.4 Section 7.1 summary · 7.2 Understanding polynomial splines and Bézier curves · 7.2.1 Explicit polynomial parametric curves · 7.2.2 Practical example: Crafting a spline for points that aren’t there yet · 7.2.3 Bézier curves · 7.2.4 Section 7.2 summary · 7.3 Understanding NURBS · 7.3.1 BS stands for “basis spline” · 7.3.2 NU stands for “nonuniform” · 7.3.3 R stands for “rational” · 7.3.4 Not-so-practical example: Building a circle with NURBS · 7.3.5 Section 7.3 summary · 7.4 Exercises · 7.5 Solutions to exercises · 7.6 Summary

What you learn in chapter 7 →

Chapter 8: Nonlinear transformations and surfaces

8.1 Polynomial transformations in multidimensional space · 8.1.1 The straightforward approach to polynomial transformations · 8.1.2 The fast and simple approach to polynomial transformations · 8.1.3 Practical example: Adding the “unbending” feature to the scanning app · 8.1.4 Section 8.1 summary · 8.2 3D surface modeling · 8.2.1 A surface is just a 3D transformation of a plane · 8.2.2 Practical example: Planting polynomial mushrooms · 8.2.3 Section 8.2 summary · 8.3 Using nonpolynomial spatial interpolation in geometry · 8.3.1 Inverse distance interpolation · 8.3.2 Inverse distance interpolation in space · 8.3.3 Practical example: Using localized inverse distance method to make a bitmap continuous and smooth · 8.3.4 Practical example: Building a deformation field with multivariable interpolation to generate better mushrooms · 8.3.5 Section 8.3 summary · 8.4 Exercises · 8.5 Solutions to exercises · 8.6 Summary

What you learn in chapter 8 →

Chapter 9: The geometry of vector algebra

9.1 Vector addition and scalar multiplication as transformations · 9.2 Dot product: Projection and angle · 9.2.1 Other names and motivations behind them · 9.2.2 Vectors and their dot product in SymPy · 9.2.3 Practical example: Using a dot product to light a scene · 9.2.4 Section 9.2 summary · 9.3 Cross product: Normal vector and the parallelogram area · 9.3.1 Other names and motivations behind them · 9.3.2 The cross product in SymPy · 9.3.3 Practical example: Check whether a triangle contains a point in 2D · 9.3.4 Section 9.3 summary · 9.4 Triple product: The parallelepiped volume · 9.4.1 The geometric sense of the triple product · 9.4.2 The triple product in SymPy · 9.4.3 Practical example: Distance to a plane · 9.4.4 Section 9.4 summary · 9.5 Generalization for parallelotopes · 9.5.1 The meaning · 9.5.2 The math · 9.5.3 The code · 9.5.4 Section 9.5 summary · 9.6 Exercises · 9.7 Solutions to exercises · 9.8 Summary

What you learn in chapter 9 →

Chapter 10: Modeling shapes with signed distance functions and surrogates

10.1 What’s an SDF? · 10.1.1 Does it have to be an SDF? · 10.1.2 How to program SDFs? · 10.1.3 Theoretical example: Making an SDF of a triangle mesh · 10.1.4 Practical example: Making an SDF of a rectangle in 2D · 10.1.5 Section 10.1 summary · 10.2 How to work with SDFs · 10.2.1 How to translate, rotate, or scale an SDF · 10.2.2 How to unite, intersect, and subtract SDFs · 10.2.3 How to dilate and erode an SDF · 10.2.4 How to hollow an SDF · 10.2.5 Section 10.2 summary · 10.3 Some techniques of not-really-SDF implicit modeling · 10.3.1 Tri-periodic minimal surfaces · 10.3.2 Practical example: A gyroid with variable thickness · 10.3.3 Metaballs · 10.3.4 Practical example: Localizing the metaballs for speed and better governance · 10.3.5 Multifocal lemniscates · 10.3.6 Practical example: A play button made of a multifocal lemniscate · 10.3.7 Section 10.3 summary · 10.4 Exercises · 10.5 Solutions to exercises · 10.6 Summary

What you learn in chapter 10 →

Chapter 11: Modeling surfaces with boundary representations and triangle meshes

11.1 Smooth curves and surfaces · 11.1.1 Data representation · 11.1.2 Operations are also data · 11.1.3 Section 11.1 summary · 11.2 Segments and triangles · 11.2.1 Vertices and triangles vs. the half-edge representation · 11.2.2 Pros and cons of triangle meshes · 11.2.3 Section 11.2 summary · 11.3 Practical example: Contouring with marching cubes and dual contouring algorithms · 11.3.1 Marching cubes · 11.3.2 Dual contouring · 11.3.3 Is it that simple in practice, too? · 11.3.4 Section 11.3 summary · 11.4 Practical example: Smooth contouring · 11.4.1 The algorithm · 11.4.2 The implementation · 11.4.3 Section 11.4 summary · 11.5 Exercises · 11.6 Solutions to exercises · 11.7 Summary

What you learn in chapter 11 →

Chapter 12: Modeling bodies with images and voxels

12.1 How does computed tomography work? · 12.2 Segmentation by a threshold · 12.3 Typical operations on 3D images: Dilation, erosion, cavity fill, and Boolean · 12.3.1 Dilation · 12.3.2 Erosion · 12.3.3 Practical example: Denoising · 12.3.4 Boolean operations on voxel models · 12.3.5 A few other uses of dilation and erosion · 12.3.6 Section 12.3 summary · 12.4 Practical example: Image vectorization · 12.4.1 Input image · 12.4.2 Step 1: Obtain a contour · 12.4.3 Step 2: Fit the contour · 12.4.4 Step 3: Make the contour smooth · 12.4.5 Implementation · 12.4.6 Section 12.4 summary · 12.5 How voxels, triangles, parametric surfaces, and SDFs work together · 12.6 Exercises · 12.7 Solutions to exercises · 12.8 Summary

What you learn in chapter 12 →

Concepts explained in this book

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