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Faculty web: [[http://www.fel.cvut.cz/cz/education/bk/predmety/46/84/p4684606.html|B4M33TDV]][[http://www.fel.cvut.cz/cz/education/bk/predmety/46/85/p4685306.html|BE4M33TDV]][[http://www.fel.cvut.cz/cz/education/bk/predmety/11/52/p11523004.html|XP33VID]]

[[https://teams.microsoft.com/l/team/19%3a7690087680764b5d9d35b4bad6633139%40thread.tacv2/conversations?groupId=d3d4842c-6d59-49b6-8115-1400800d5c11&tenantId=f345c406-5268-43b0-b19f-5862fa6833f8|Distant teaching in MS Teams]]
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====== TDV − 3D Computer Vision (Winter 2020) ======

**<fc #ff0000>Important</fc>**: Due to official regulations, the lectures and labs will proceed remotely in an on-line way. We will use the MS Teams tool both for lectures and labs. All enrolled students should have access to 'Team-TDV' team in the MS Teams application. There is also direct link above.
===== 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. 

|<imgcaption image_asia_img|an example of input images>{{:courses:tdv:asia-images.jpg|}}</imgcaption>|<imgcaption image_asia_vrml|resulting vrml model>{{:courses:tdv:asia.gif|}}</imgcaption>|


===== Lectures: Tuesday 12:45-14:15 =====
Place: <del>KN:E-127</del> <fc #ff0000>on-line, see the link above</fc>

Lecturer: [[courses:tdv:start#Contacts|Radim Šára]]

Updated lecture slides are ready for download before the lecture. They get annotated during the lecture and appear here after the lecture.

**<fc #ff0000>New:</fc>** {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-all.pdf| All slides in a single file (last updated 2021-01-05, without course overview)}}

^  Week ^  Date  ^  Updated Slides  ^ Annotated Slides ^ Recording ^ Lecture Content  ^
|  01  |  22.09. | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-01a.pdf|Introduction}} (large!) {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-01b.pdf|Course Overview}} |  |  | 3D computer vision, its goals and applications, course overview  |
| :::   | :::     | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-01c.pdf|Lecture 1}} | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-01c-annotated.pdf|L1}} | [[http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-01.mp4|R1]] | basic geometry of points and lines |
|  02  |  29.09. | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-02.pdf|Lecture 2}} | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-02-annotated.pdf|L2}} | [[http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-02.mp4|R2]] |homography, perspective camera, projection matrix decomposition  |
|  03  |  06.10. | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-03.pdf|Lecture 3}} | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-03-annotated.pdf|L3}} | [[http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-03.mp4|R3]] |optical center, optical ray, axis, plane; vanishing point, cross-ratio |
|  04  |  13.10. | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-04.pdf|Lecture 4}} | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-04-annotated.pdf|L4}} | [[http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-04.mp4|R4]] |camera calibration from vanishing points, camera resection from 6 points, critical configurations for resection, the exterior orientation problem |
|  05  |  20.10. | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-05.pdf|Lecture 5}} | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-05-annotated.pdf|L5}} | [[http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-05.mp4|R5]] |the relative orientation problem, epipolar geometry, epipolar constraint, essential matrix decomposition |
|  06  |  27.10. | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-06.pdf|Lecture 6}} | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-06-annotated.pdf|L6}} | [[http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-06.mp4|R6]] |7-point algorithm for fundamental matrix estimation, 5-point algorithm for essential matrix estimation, triangulation by algebraic error minimization |
|  07  |  03.11. | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-07.pdf|Lecture 7}} | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-07-annotated.pdf|L7}} | [[http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-07.mp4|R7]] | reprojection error, Sampson error correction, the golden standard triangulation method, local optimization for fundamental matrix estimation |
|  08  |  10.11. | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-08.pdf|Lecture 8}} | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-08-annotated.pdf|L8}} | [[http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-08.mp4|R8]] | joint matching and epipolar geometry estimation, robust error function, optimization by random sampling |
|  09  |  17.11. | //National holiday// ||
|  10  |  24.11. | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-10.pdf|Lecture 10}} | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-10-annotated.pdf|L10}} | [[http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-10.mp4|R10]] | MH sampler, RANSAC |
|  11  |  01.12. | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-11.pdf|Lecture 11}} | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-11-annotated.pdf|L11}} | [[http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-11.mp4|R11]] |camera system reconstruction, bundle adjustment, gauge freedom in bundle adjustment |
|  12  |  08.12. | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-12.pdf|Lecture 12}} | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-12-annotated.pdf|L12}} | [[http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-12.mp4|R12]] | minimal representations, introduction to stereovision | 
|  13  |  15.12. | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-13.pdf|Lecture 13}} | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-13-annotated.pdf|L13}} | [[http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-13.mp4|R13]] | epipolar rectification, occlusion constraints |
|  14  |  05.01. | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-14.pdf|Lecture 14}} | {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-14-annotated.pdf|L14}}  | [[http://cmp.felk.cvut.cz/cmp/courses/TDV/2020W/lectures/tdv-2020-14.mp4|R14]] | matching table, Marroquin's WTA matching algorithm, maximum-likelihood matching algorithm, ordering constraint, stereo matching algorithm comparison |



{{http://cmp.felk.cvut.cz/cmp/courses/TDV/2010W/lectures/3DV-slovnik.pdf|The English-Czech and Czech-English dictionary of 3D Vision}} and {{http://cmp.felk.cvut.cz/cmp/courses/TDV/2010W/lectures/3DV-slovnik-booklet.pdf| its print-ready A5 booklet version}}


===== Exercises (requirements) =====

Teacher: [[courses:tdv:start#Contacts|Martin Matoušek]]

Details about exercises (technical content and assessment) are in the separate section [[courses:tdv:labs:start|Exercises]].

**Notice**: according to the study and examination code of CTU((Rev. Oct 1, 2015, in Czech, or Rev, Oct 1, 2015, in English: Article 7, Paragraph 5)), 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 ===

  - Attending the exercises is mandatory, two absences are allowed. 
  - Submission/presentation of all required intermediate results.
  - Submission of all required elementary methods that must pass automatic check.
  - Submission of results of the term project.
  - 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 ^

/* The answers to bonus-point questions from the lectures are due on January 14, 2018 (23:59) at the latest. */

[[courses:tdv:labs:start#Assessment_of_Exercises|Assessment of Exercises]] is described in detail in the section of exercises. 
/*{{http://cmp.felk.cvut.cz/cmp/courses/TDV/2010W/lectures/zkouska-info.pdf|Informace o zkoušce}}.*/

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). 

/*
, s omezením na minimální počet bodů z ústní zkoušky (viz informace).

Celkově získané body včetně bonusu se shora omezí hodnotou 100, případné desetiny se zaokrouhlí aritmeticky a výsledná známka je pak standardně dána podle ECTS stupnice (tedy 100−90⇒A, 89−80⇒B, 79−70⇒C, 69−60⇒D, 59−50⇒E, ≤ 49 ⇒ F), s omezením na minimální počet bodů z ústní zkoušky (viz informace).
*/

===== 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.

/*
{{http://cmp.felk.cvut.cz/cmp/courses/TDV/2010W/lectures/zkouska-info.pdf|průběh a pravidla zkoušky}}
*/

===== 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**|
|''sara@cmp.felk.cvut.cz''|''Martin.Matousek@cvut.cz''|
|KN, room 103|Dejvice, CIIRC, room B606|
| [[https://teams.microsoft.com/l/meetup-join/19%3a5655b69feb954411b99c5a54119520b1%40thread.tacv2/1611242368110?context=%7b%22Tid%22%3a%22f345c406-5268-43b0-b19f-5862fa6833f8%22%2c%22Oid%22%3a%22cb5ffe8d-db86-40c2-b770-77331d93a368%22%7d|virtual office]] | |
|phone (22435) 7203|phone (22435) 4221| 
| [[https://usermap.cvut.cz/profile/84f72014-954f-4b79-ae1d-e8a330c63fb3|Usermap]] | [[https://usermap.cvut.cz/profile/daecc478-57b0-4918-af30-618b1a9179dc|Usermap]] |
|  [[http://cmp.felk.cvut.cz/~sara|{{:courses:tdv:rs-2-small.png|}}]]  |  {{:courses:tdv:martin_matousek.png|}}  |

/*
|{{misc:projects:oppa_oi_english:3_loga_velikost_196.png}}|<wrap lo>Podpořeno [[http://www.prahafondy.eu/cz/oppa.html|OPPA]]. Spolufinancováno Evropským sociálním fondem. Praha & EU: Investujeme do vaší budoucnosti.</wrap>|
*/