# Cyrill Stachniss

Credits to Sachin. See his page for the actual courses that are recommended.

### Lecture Notes

Note: I try to follow Drake Notation for the order of the transforms. So from Cyrill notation to drake notation:

The transform tells you how to go from to , but you think about it in terms of measuring frame in terms of .

#### Camera Basics and Propagation of Light

Notes moved to Camera.

#### Local Operators Through Convolutions - Part 1: Smoothing

I wasnâ€™t going to go through this chapter. But then the teacher was going through the concepts of kernels and filter for the geometric transformations lecture, and I never heard about the box filter.

What is the difference between a filter and a kernel?

They are often used interchangeably.

How is an image actually smoothed?

• I thought it was just resampling

So how is this chapter relevant?

There are 3 types of operators

1. Point operators
2. Local operators
3. Global operators

We can use these for noise reduction in an image.

##### Box Filter

See Box Filter.

Notes on Convolution.

#### Geometric Transforms of Images

I already know my transforms, but it is just a matter of notation.

• from frame to frame

Though I donâ€™t like this notation. Why is the â€śtoâ€ť on the left..?

• So the confusion is that they say â€śfrom to â€ť, but if you actually look at the transformation itself, the values will be measured from to â€¦

I will use the Drake Notation.

Generally, the transformed pixel coordinates are no longer integers. What should we do?

• My first instinct is to just round the number. But think about what is actually happening under the hood:
• Imagine you scale down an image. There are going to be multiple pixels intensities that map to the same location. Which pixel intensity do you pick?

The solution: resampling

##### Resampling

Ways to do this:

Okay, but how do we map the values?

• I thought it was just multiplying each pixel by a certain value
• Think about correcting for distortions

Ahh, I think that the interpolation step happens with resampling.

• You have irregular points
• It needs to be clear what mapping direction there is

This is important for scale invariance. But the other option is to use an Image Pyramid.

#### Camera Parameters - Extrinsics and Intrinsics

Notes moved to Camera Calibration.

#### Visual Feature Part 1: Computing Keypoints

Notes moved to Keypoint.

#### Visual Feature Part 2: Feature Descriptors

https://www.ipb.uni-bonn.de/html/teaching/photo12-2021/2021-pho1-11-features-descriptors.pptx.pdf

#### Math Basics Often Used In Photogrammetry

TODO: This will help me understand why we use Eigenvalues

#### Direct Linear Transform for Camera Calibration and Localization

This is really fundamental. Notes in Direct Linear Transform.

#### Least Squares - An Informal Introduction (Cyrill Stachniss)

This is really useful for a handful of problems for state estimation

Graph-Based SLAM is the least-squares approach to SLAM

Didnâ€™t finish, focusing on camera calibration part

#### Camera Calibration using Zhangâ€™s Method (Cyrill Stachniss)

Notes in Zhangâ€™s Method.

#### Projective 3-Point Algorithm

This is how you actually infer the position of the camera.

I want to glance at this to understand the implementation of this at an architecture level.

Notes in Projective 3-Point Algorithm.