### Table of Contents

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# TDV − 3D Computer Vision (Winter 2022)

## Motivation

This course introduces methods and algorithms for 3D geometric scene reconstruction from images. The student will understand these methods and their essence well enough to be able to build variants of simple systems for the reconstruction of 3D objects from a set of images or video, for inserting virtual objects to video-signal source, or for computing ego-motion trajectory from a sequence of images. The labs will be hands-on, the student will gradually build a small functional 3D scene reconstruction system.

 Fig. 1: an example of input images Fig. 2: resulting vrml model

## Lectures: Tuesday 12:45-14:15

Location: KN:E-112

Lecturer: Radim Šára

Updated lecture slides are ready for download before the lecture. They get annotated during the lecture and appear here after the lecture. Recordings are from the previous course run and are meant as supporting material, not a substitute for lectures. The live version may differ from the recordings.

Week Date Updated Slides Annotated Slides Lecture Content
01 20.09. Introduction
(videos)
Course_Overview
3D computer vision, its goals and applications, course overview
Lecture 1 L1 basic geometry of points and lines
02 27.09. Lecture 2 L2 homography, perspective camera, projection matrix decomposition
03 04.10. Lecture 3 L3 optical center, optical ray, axis, plane; vanishing point, cross-ratio
04 11.10. Lecture 4 L4 camera calibration from vanishing points, camera resection from 6 points, critical configurations for resection, the exterior orientation problem
05 18.10. Lecture 5 L5 the relative orientation problem, epipolar geometry, epipolar constraint
06 25.10. Lecture 6 L6 essential matrix decomposition, 7-point algorithm for fundamental matrix estimation, 5-point algorithm for essential matrix estimation, triangulation by algebraic error minimization
07 01.11. Lecture 7 L7 reprojection error, Sampson error correction, the golden standard triangulation method, local optimization for fundamental matrix estimation
08 08.11. Lecture 8 L8 joint matching and epipolar geometry estimation, robust error function, optimization by random sampling
09 15.11. Lecture 9 L9 MH sampler, RANSAC, camera system reconstruction from triples
10 22.11. Lecture 10 L10 camera system reconstruction from pairs, bundle adjustment
11 29.11. gauge freedom in bundle adjustment, minimal representations
12 06.12. introduction to stereovision, epipolar rectification
13 13.12. occlusion constraints, matching table, Marroquin's WTA matching algorithm, maximum-likelihood matching algorithm, ordering constraint, stereo matching algorithm comparison
14 10.01. reserve

## Exercises (requirements)

Teacher: Martin Matoušek

Details about exercises (technical content and assessment) are in the separate section Exercises.

Notice: according to the study and examination code of CTU1), attendance at lectures is not mandatory (but recommended). However, students attending exercises are required to be theoretically prepared. The necessary theory is explained at the preceding lectures and can be also found in the recommended literature.

#### Requirements for the Credit

1. Attending the exercises is mandatory, two absences are allowed.
2. Submission/presentation of all required intermediate results.
3. Submission of all required elementary methods that must pass automatic check.
4. Submission of results of the term project.
5. Submission of all homework problems assigned during lectures.

## Assessment

Student assessment is based on scoring in the nominal range 0−100 points. There is also possibility to obtain some additional bonus points. The points are allocated to lectures, labs, homework problems and exam as follows:

Nominal points Minimal points Bonus points
Exercises 45
Homework assignments given at lectures 9 14
Midterm test 10 3
Exam test 24 6
Exam – oral 12
Total 100 +14

Assessment of Exercises is described in detail in the section of exercises.

The total of all points, including the bonuses is arithmetically rounded up and clipped at 100.

The grade is then given by the standard table (100−90⇒A, 89−80⇒B, 79−70⇒C, 69−60⇒D, 59−50⇒E, ≤ 49 ⇒ F).

## Exam

The first test is done during the semester. The second test is a part of the exam at the end. The exam has two parts, usually, one day we do the test and the other day we have the oral part. The oral part is mandatory to achieve the A–B grades; it tests the ability to solve small problems; at least 5 points must be achieved, otherwise the final grade is C.

## Additional Info

There is also a discussion forum (see link in the page head). Questions, feedback and comments on lectures or exercises are welcome.

## Contacts

 Lectures: Radim Šára Exercises: Martin Matoušek Exercises: Jaroslav Moravec sara@cmp.felk.cvut.cz Martin.Matousek@cvut.cz moravj34@fel.cvut.cz KN, room 103 Dejvice, CIIRC, room B606 KN, room 103 virtual office Virtual office phone (22435) 7203 phone (22435) 4221 Usermap Usermap Usermap
1)
Rev. Oct 1, 2015, in Czech, or Rev, Oct 1, 2015, in English: Article 7, Paragraph 5
courses/tdv/start.txt · Last modified: 2022/11/22 20:58 by sara