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Schedule: B4M33TDV BE4M33TDVXP33VID
Students: B4M33TDV BE4M33TDVXP33VID
Faculty web: B4M33TDVBE4M33TDVXP33VID

# TDV − 3D Computer Vision (Winter 2019)

## 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 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 be gradually building 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, KN:E-126

Updated lecture slides are ready for download before the lecture. They get annotated during the lecture and appear here after the lecture. All slides in a single file (last updated 2020-01-07, without course overview)

Week Date Updated Slides Annotated Slides Lecture Content
1 24.9. Introduction (large!) Course Overview 3D computer vision, its goals and applications, course overview
Lecture 1 L1 basic geometry of points and lines, homography
2 1.10. Lecture 2 L2 perspective camera, projection matrix decomposition, optical center
3 8.10. Lecture 3 L3 optical ray, axis, plane; vanishing point, cross-ratio
4 15.10. Lecture 4 L4 camera calibration from vanishing points, camera resection from 6 points, critical configurations for resection
5 22.10. Lecture 5 L5 the exterior orientation problem, the relative orientation problem, epipolar geometry, epipolar constraint
6 29.10. Lecture 6 L6 essential matrix decomposition, 7-point algorithm for fundamental matrix estimation, 5-point algorithm for essential matrix estimation
7 5.11. Lecture 7 L7 triangulation by algebraic error minimization, reprojection error, Sampson error correction
8 12.11. Lecture 8 L8 the golden standard triangulation method, local optimization for fundamental matrix estimation, robust error function
9 19.11. Lecture 9 L9 optimization by random sampling, MH sampler, RANSAC
10 26.11. no lecture midterm test @ exercises
11 3.12. Lecture 10 L10 camera system reconstruction
12 10.12. Lecture 11 L11 bundle adjustment, gauge freedom in bundle adjustment, minimal representations, introduction to stereovision
13 17.12. Lecture 12 L12 epipolar rectification, occlusion constraint
14 7.1. Lecture 13 L13 matching table, Marroquin's WTA matching algorithm, maximum-likelihood matching algorithm, ordering constraint, stereo matching algorithm comparison

## 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.

 Lectures: Radim Šára Exercises: Martin Matoušek sara@cmp.felk.cvut.cz Martin.Matousek@cvut.cz KN, room 103 Dejvice, CIIRC, room B606 phone (22435) 7203 phone (22435) 4221 Usermap Usermap