# Photogrammetry I & II Course (2021/22)

### Introduction

#### Introduction to Photogrammetry (Cyrill Stachniss)

Introductory Lecture for the Photogrammetry Module consisting of the courses Photogrammetry I and & at the University of Bonn.

#### Photogrammetry Course – Lecture & Tutorial Information for Students Enrolled at the University of Bonn

Notes about lectures, tutorials, homework assignments, and formal aspects for the Photogrammetry I & II Course, taught in the BSc programme Geodesy and Geoinformation at the University of Bonn for the summer term 2021 and winter term 2021/2022.

Python Crash Course (external video)

### Python Crash Course (cont.)

Python Crash Course (external video)

#### Jupyter Notebook Lab Tutorial

Python Crash Course (external video)

#### Python NumPy for Beginners

Python Crash Course (external video)

### Technical Content on Photogrammetry Starts

#### What Cameras Measure – 5 Minutes with Cyrill

What do cameras actually measure explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Camera Basics and Propagation of Light (Cyrill Stachniss)

Camera Basics and Propagation of Light

### Week 4

#### Image Histograms – 5 Minutes with Cyrill

Image histograms explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Image Histograms – Part1: Histograms and Point Operators (Cyrill Stachniss)

Image Histograms Part 1: Image Histograms and Simple Point Operators

#### Image Histograms – Part2: Histograms Transformations (Cyrill Stachniss)

Image Histograms – Part 2: Histograms Transformations, Histogram Equalizations and Noise Variance Equalizations

### Week 5

#### Binary Images (Cyrill Stachniss)

Binary Images and Commonly used Operations: Connected Components, Distance Transform, Morphological Operators

#### Local Operators Through Convolutions – Part 1: Smoothing (Cyrill Stachniss)

Local operators defines in the framework of convolutions looking into two smoothing kernels, namely the box filter and binomial filter.

### Week 6

#### Local Operators Through Convolutions – Part 2: Gradient Filters (Cyrill Stachniss)

Local operators defines in the framework of convolutions looking into gradient kernels such as Sobel, Scharr, or Laplace. The video basically explains how to compute a derivative of an image.

#### Geometric Transformation of Images (Cyrill Stachniss)

Geometric Transformation of Images

### Week 7

#### Image Matching using Cross Correlation (Cyrill Stachniss)

Image Matching using Cross Correlation

#### Visual Feature Part 1: Computing Keypoints (Cyrill Stachniss)

Visual Feature Part 1: Computing Keypoints

### Week 8

#### SIFT – 5 Minutes with Cyrill

SIFT features explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Binary Features – 5 Minutes with Cyrill

Binary features explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Visual Features Part 2: Features Descriptors (Cyrill Stachniss)

Visual Features Part 2: Features Descriptors

#### Image Segmentation using Mean Shift (Cyrill Stachniss)

Image Segmentation using the Mean Shift Algorithm

### Week 9

#### Introduction to Classification (Nived Chebrolu)

Introduction to Classification

#### Classification – Ensemble Methods (Nived Chebrolu)

Classification – Ensemble Methods

### Week 10

#### Neural Networks – 5 Minutes with Cyrill

Neural networks explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Introduction to Neural Networks – Part 1: The Basics (Cyrill Stachniss)

Lecture on Introduction to Neural Networks – What are neural networks and how do they work covering MLPs, weights, biases, and activations and examples how the hidden layers of a network look like.

#### Gradient Descent – 5 Minutes with Cyrill

Gradient descent explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Backpropagation – 5 Minutes with Cyrill

Backpropagation explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Introduction to Neural Networks – Part 2: Learning (Cyrill Stachniss)

Lecture on Introduction to Neural Networks – Part 2: Learning (Parameter Learning, Stochastic Gradient Descent, Backprop)

Errata in the video (corrected in the pdf file of the slides):
* At 55:23 the value of dL\df is not specified and only indicated as “…”. This is suboptimal for the example as this value has to be multiplied with dL\da and dL\db. Thus, the example might be a bit misleading.
* At 59:37 the derivative of “z^2” is “2z” and not “z”, thus the last dimension of the gradient in the example must be multiplied with 2.

### Week 11

#### Convolutional Neural Networks – 5 Minutes with Cyrill

Convolution Neural Networks (CNNs) explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Introduction to Neural Networks – Part 3: CNNs (Cyrill Stachniss)

Lecture on Introduction to Neural Networks – Part 3: Convolutional Neural Networks

#### Some Math Basics often used in Photogrammetry (Cyrill Stachniss)

An brief, informal, and incomplete collection of math basics and tools that are often used in Photogrammetry
Cyrill Stachniss, 2021

#### Homogeneous Coordinates – 5 Minutes with Cyrill

Homogeneous coordinates explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Homogeneous Coordinates (Cyrill Stachniss)

Lecture on Homogeneous Coordinates

### Week 12

#### Camera Intrinsics and Extrinsics – 5 Minutes with Cyrill

Intrinsic and extrinsic parameters of a camera explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Mapping the 3D World to an Image – 5 Minutes with Cyrill

Mapping 3D points to 2D pixel locations explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Camera Parameters – Extrinsics and Intrinsics (Cyrill Stachniss)

Camera Parameters – Extrinsic and Intrinsic Parameters

#### Direct Linear Transform – 5 Minutes with Cyrill

The Direct Linear Transform or short DLT explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Direct Linear Transform for Camera Calibration and Localization (Cyrill Stachniss)

Direct Linear Transform – Joint Camera Calibration and Localization

### Week 13

#### Intrinsic Camera Calibration – 5 Minutes with Cyrill

Intrinsic camera calibration explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Camera Calibration using Zhang’s Method (Cyrill Stachniss)

Camera Calibration using Zhang’s Method

#### Projective 3 Point Algorithm – 5 Minutes with Cyrill

Projective 3 Point (P3P) algorithm explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Projective 3-Point Algorithm using Grunert’s Method (Cyrill Stachniss)

Projective 3-Point Algorithm, also called Spatial Resectioning, using Grunert’s Method of 1841

### Week 1

#### Photogrammetry II Course – Welcome (Cyrill Stachniss)

Welcome to the Photogrammetry II Course
Slides: PDF-01

#### Fundamental and Essential Matrix – 5 Minutes with Cyrill

Fundamental and essential matrix explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Relative Orientation, Fundamental and Essential Matrix (Cyrill Stachniss)

Relative Orientation of the Image Pair, Fundamental and Essential Matrix
Slides: PDF-02

### Week 2

#### Epipolar Geometry Basics (Cyrill Stachniss)

Epipolar Geometry Basics
Slides: PDF-03

#### 8 Point Algorithm – 5 Minutes with Cyrill

8 point algorithm explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Direct Solution for Estimating the Fundamental and Essential Matrix (Cyrill Stachniss)

Direct Solution for Estimating the Fundamental and Essential Matrix from Corresponding Points (“8-Point Algorithm”)
Slides: PDF-04

### Week 3

#### Iterative Solution for Estimating the Relative Orientation (Cyrill Stachniss)

Iterative Solution for Estimating the Relative Orientation
Slides: PDF-05

### Week 4

#### RANSAC – 5 Minutes with Cyrill

RANSAC – Random Sample Consensus explained in 5 minutes
Series: 5 Minutes with Cyrill

#### RANSAC – Random Sample Consensus (Cyrill Stachniss)

RANSAC – Random Sample Consensus
Slides: PDF-06

### Week 5

#### Stereo Normal Case – 5 Minutes with Cyrill

Stereo normal case, a special configuration of a stereo camera, explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Triangulation for Image Pairs (Cyrill Stachniss)

Triangulation of 3D Points based on Pairs of Camera Images
Slides: PDF-07

### Week 6

#### Absolute Orientation Problem – 5 Minutes with Cyrill

Absolute orientation problem explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Absolute Orientation Problem: Similarity Transformations Between Point Sets (Cyrill Stachniss)

Direct solution to the absolute orientation problem
Slides: PDF-08

### Week 7

#### Bundle Adjustment – 5 Minutes with Cyrill

Bundle Adjustment explained in 5 minutes
Series: 5 Minutes with Cyrill

Slides: PDF-09

Slides: PDF-10

### Week 9

#### Orthophoto – 5 Minutes with Cyrill

Orthophoto explained in 5 minutes
Series: 5 Minutes with Cyrill

Orthophotos
Slides: PDF-11

### Week 10

#### k-means Clustering – 5 Minutes with Cyrill

k-means Clustering explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Bag of Visual Words – 5 Minutes with Cyrill

Bag of visual words explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Bag of Visual Words (Cyrill Stachniss)

Lecture on Bag of Visual Words for Finding Similar Images
Slides: PDF-12

### Week 11

#### OPTIONAL VIDEO (Probability Primer)

A Short Probability Primer

#### Bayes Filter – 5 Minutes with Cyrill

Bayes Filter explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Bayes Filter (Cyrill Stachniss)

Derivation of the Bayes filter equation
Slides: PDF-13

### Week 12

#### Kalman Filter – 5 Minutes with Cyrill

The Kalman filter explained in 5 minutes
Series: 5 Minutes with Cyrill

#### Kalman Filter & EKF (Cyrill Stachniss)

Kalman Filter and Extended Kalman Filter (EKF)
Slides: PDF-14

### Week 13

#### SLAM – 5 Minutes with Cyrill

SLAM explained in 5 minutes
Series: 5 Minutes with Cyrill

There is also a set of more detailed lectures on SLAM available:

#### Introduction to SLAM from a Photogrammetric Perspective (Cyrill Stachniss)

A Brief Introduction to SLAM from a Photogrammetric Perspective
Slides: PDF-15

### Week 14

#### EKF-SLAM (Cyrill Stachniss)

EKF-SLAM: Landmark-based SLAM using the Extended Klaman Filter
Slides: PDF-16

#### Photogrammetry II Course – Thank You for Your Attention (Cyrill Stachniss)

A few final words on the Photogrammetry II Course
Cyrill Stachniss