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Schedule: B4M33TDV BE4M33TDVXP33VID

Students: B4M33TDV BE4M33TDVXP33VID

Faculty web: B4M33TDVBE4M33TDVXP33VID

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.

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.

All slides (last updated 2023-12-19, without course overview)

Week | Date | Updated Slides | Annotated Slides | Lecture Content |
---|---|---|---|---|

01 | 26.09. | Introduction (videos) Course_Overview | 3D computer vision, its goals and applications, course overview | |

Lecture 1 | L1 | basic geometry of points and lines, homography | ||

02 | 03.10. | Lecture 2 | L2 | perspective camera, projection matrix decomposition, optical center, optical ray, axis |

03 | 10.10. | Lecture 3 | L3 | optical plane; vanishing point, cross-ratio, camera calibration from vanishing points |

04 | 17.10. | Lecture 4 | L4 | camera resection from 6 points, critical configurations for resection, the exterior orientation problem, the relative orientation problem |

05 | 24.10. | Lecture 5 | L5 | epipolar geometry, epipolar constraint, representation theorem |

06 | 31.10. | 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 | 07.11. | Lecture 7 | L7 | reprojection error, Sampson error correction, the golden standard triangulation method, local optimization for fundamental matrix estimation |

08 | 14.11. | Lecture 8 | L8 | joint matching and epipolar geometry estimation, robust error function, optimization by random sampling, RANSAC, MH sampler |

09 | 21.11. | Lecture 9 | L9 | camera system reconstruction from triples and pairs |

10 | 28.11. | Lecture 10 | L10 | bundle adjustment, gauge freedom in bundle adjustment, minimal representations, matrix Lie groups, motion representation in SO(3) and SE(3) |

11 | 05.12. | Lecture 11 | L11 | epipolar rectification, disparity, introduction to stereovision |

12 | 12.12. | Lecture 12 | L12 | occlusion constraints, matching table, Marroquin's WTA matching algorithm, maximum-likelihood matching algorithm |

13 | 19.12. | Lecture 13 | L13 | ordering constraint, stereo matching algorithm comparison; bonus: radial distortion models for cameras; the course summary; Bonus: radial distortion models |

14 | 09.01. | no lecture |

The English-Czech and Czech-English dictionary of 3D Vision and its print-ready A5 booklet version

Teacher: Martin Matoušek, Jaroslav Moravec

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

**Notice**: according to the study and examination code of CTU^{1)}, 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.

- 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.

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 | 7 | 16 | |

Midterm test | 11 | 3 | |

Exam test | 24 | ||

Exam – oral | 13 | ||

Total | 100 | +16 |

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).

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.

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

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 |

Rev. Oct 1, 2015, in Czech, or Rev, Oct 1, 2015, in English: Article 7, Paragraph 5

courses/tdv/start.txt · Last modified: 2023/12/19 15:33 by sara