Learning Objectives

Introduction¶

I understood that an image is a projection from 3D to 2D, and therefore information is lost.
I know how a projection can be calculated using matrix multiplication.

I know that there are internal and external camera parameters and how they are related.

I learned that distortion of an image can occur due to perspective as well as the lens.
I learned about a method that can correct distortion in wide-angle shots.

I understood that projective geometry can be used to easily calculate intersections of lines in images.
I can imagine that it also works in 3D with planes.

I have understood how to use vanishing points and a reference object to measure the height of other objects in an image.
I know how to calculate the position of a vanishing point.
I know what an artificial neuron is and I can imagine that millions of them can be connected to each other.
I understand why it is called “deep” learning.
I know why you should never use training data for testing.
I understand what training, testing and prediction mean and can understand the process.
I understand the role of the loss function.
I know what the aim and principle of the gradient descent method is and have understood what it has to do with derivatives.
I know that back propagation is the algorithm used to train a neural network.
I know about DepthAnything and other deep learning based methods to get a depth map from a single image.

I have understood the principle of epipolar geometry.
I have understood the purpose and use of rectification.
I have understood why stereo matching is so difficult, especially on edges.
I know the distinction between local and global stereo algorithms.
I understand how a global stereo matching can be found using dynamic programming on the disparity space image.

I understand the calculation process of stereo triangulation and can reproduce it.
I understand the link between structured illumination and stereo triangulation.
I understand the idea of using binary codes for Structured Light scanners and why this speeds up the process.
I have understood how triangulation can be used to reconstruct the shape of objects and I have learned about various applications.

I have understood what epipoles are.
I have learned that the mapping from one point to the epipolar line in another image is described by the fundamental matrix.
I can estimate where the epipolar lines are in two images of the same scene.
I know that you can rectify a stereo image pair if you know the fundamental matrix.
I have understood the difference between essential matrix and fundamental matrix.
I understood that you can calculate the fundamental matrix from matching image points.
I learned that it is still a relatively simple minimization problem.

I have understood that multi-view stereo assumes that one knows the intrinsic and extrinsic camera parameters.
I learned about different weighting functions for matching.
I learned that you can reconstruct very accurately with many cameras and I know some applications.
I learned about the influence of the base length.
I have understood the principle of how to arrive at 3D models instead of a single depth map.

I understand what is meant by image feature.
I am aware of different applications that use image features and am motivated to learn more about features.
I understand what are the advantages of image features over individual pixels, regions or whole images.
I can understand the approach; detection, descriptive, matching.
I know what a blob is and know and understand the concept of scale invariance.
I have the understood what invariance is and what it is necessary for.
I know applications based on finding pairs of features.
I understand how invariance can be achieved by the descriptor.
I know SIFT as a detector and descriptor.
I can calculate the distance between two features.
I understood that there is problem with repetitions and know possible solutions of the problem.
I understand RANSAC and have ideas about where to use it.
I go through the day exhilarated and whistling the RANSAC song!
I can distinguish between feature detection and feature description.

I know what Structure From Motion means and why it is called what it is called.
I understand that you can take advantage of the low rank when solving the system of equations.
I understand that there is a lot of data and large matrices that need to be computed together and that is why iterative methods are used.
I know many different applications of SfM.

I know what data structures exist for 3D data.
I understand how the ICP algorithm works.
I know octrees.
I know how a kD tree is constructed and how to find the nearest neighbors in it.