Agenda (2025 fall)

Week	Lecture	Laboratory
1st – Registration week
2nd	Introduction	Vehicle and sensor kit demonstration ( videos of 2023)
3rd	Estimation theory: inhomogeneous linear systems	Intro to 3D Computer Vision
4th	Circle Estimation Lagrange multiplier technique.	Affine/Perspective transformations
5th	Camera models.	RANSAC
6th	Back-projection, Homographies.	Point-cloud visualization
7th	Homographies (cont.), homography estimation. Data normalization.	Multi-model fitting
8th	Optimal line/plane fitting. Singular Value Decomposition. Introduction to Camera Calibration.	Homography Estimation
9th	break	break
10th	Camera Calibration. Introduction to stereo vision.	Homography Estimation / Quiz-retake
11th	Deep Dive in stereo Vision	Camera Calibration
12th	Stereo Vision: triangulation, essential matrix decomposition	Stereo Vision #1 (Epipolar geometry)
13th
14th
15th

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Rules for quizzes

There are at least 10 quizzes in the semester.  They should be filled in at the beginning of the laboratories, online attendance is not allowed. 

There are 5 questions in each quiz. You get 

• +1 point: if four or five answers are correct. 

• 0 point if you give three good answers 

• -1 point: less than three correct ones 

At the end, all the points are summed.  You have to reach +5 points, in total, to pass the subject.  Theoretically, the maximum is +10 points. The points do not count for your final grade, quizzes are to check the minimal requirements. 

If you miss/fail a quiz, you can do it later again. There will be four special weeks when you can repeat one quiz. (One retake/day) 

Oral exams :

• later…

Assignment presentation

• later…

Topics for oral exam:

1. Basic estimation theory: solution for homogeneous and inhomogeneous linear system of equations.
2. Robust fitting methods: the RANSAC method.
3. Multi-model fitting: sequential RANSAC, MultiRANSAC
4. Camera models: perspective camera, orthogonal projection, weak-perspective camera.
5. Calibration of perspective camera using a spatial calibration object: Estimation and decomposition of projection matrix.
6. Chessboard-based calibration of perspective cameras. Radial and tangential distortion.
7. Plane-plane homography. Panoramic images by homographies.
8. Estimation of homography. Data normalization.
9. Basics of epipolar geometry. Essential and fundamental matrices. Rectification.
10. Estimation of essential and fundamental matrices. Data normalization. Decomposition of essential matrix.
11. Triangulation for both standard and general stereo vision.
12. Stereo vision for planar motion.
13. Tomasi-Kanade factorization: multi-view reconstruction by orthogonal and weak-perspective camera models.
14. Reconstruction by merging stereo reconstructions. Registration of two point sets by a similarity transformation.
15. Numerical optimization
16. Numerical multi-view reconstruction: bundle adjustment.
17. Tomasi-Kanade factorization with missing data.
18. Reconstruction by special devices: laser scanning, depth camera, LiDAR.

Lectures: on Wednesdays, 18:45-20:15 , lecture hall 0-805 Fejér Lipót (South Building)

Practices:

• Group #1: on Tuesdays 10.15-11.45, 2.218 Computer Algebra lab. (South Building)
• Group #2: on Fridays 8.15-9.45, 7-89 AI lab. (North Building)
• Group #3: on Fridays 10.15-11.45, 7-15 PC11 (North Building)

Final grade

The final grade is the sum of oral exam (max. 50%) and assignment scores. At least 40% (20+20) should be reached both from oral exam and assignment scores. There will be minimal questions for the oral exam. Questions are published later.

Thresholds for marks as follows:

• Excellent (5): >=85%
• Good (4): 70-84%
• Satisfactory (3): 55-69%
• Pass (2): 40-64%
• Fail (1): <40%

Agenda (2025 spring)

Week	Lecture	Laboratory
1st – Registration week
2nd	Introduction	Intro to 3D Computer Vision
3rd	Estimation theory: inhomogeneous linear systems	Vehicle and sensor kit demonstration ( videos of 2023)
4th	Circle Estimation Lagrange mutliplier technique.	Affine/Perspective transformations
5th	Robust fitting: RANSAC method	RANSAC
6th	Homogeneous systems, Lagrange multipliers. Point-line/plane distances. Optimal line/plane fitting.	Point-cloud visualization
7th	Singular Value Decomposition. Camera models.	Multi-model fitting
8th	Homography Estimation. Data normalization.	Homography Estimation
9th	Camera Calibration. RQ-decomposition	Camera Calibration
10th	Radial-Tangential distortion. Introduction to stereo vision	Point registration: theory & source code
11th	Stereo vision	Stereo Vision Triangulation
12th	Break.	Break
13th	Stereo Vision.	—
14th	Planar Motion. Numerical Optimization.	Object detection
15th	Bundle adjustment. Tomasi-Kanade factorization	Tomasi-Kanade factorization. C++ source for windows, linux

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Fall semester of 2024

3D Computer Vision

Lectures: on Mondays, 8:25-9:55 , lecture hall 0.79  0.79 Jánossy Lajos lecture hall,  (North Building)

Practices:

• On Wednesdays 17.55-19.25, South building at
  • South building 0-311 Konig terem room
  • South building 0-312 Gallai Tibor room
  • (South building 0-220 Kárteszi Ferenc room)

Teachers:

• Levente Hajder
• Tamás Tófalvi
• Tarlan Ahadli

Demonstrators

• Máté Poór
• Muhammad Rafi Faisal

Agenda

Week	Lecture	Laboratory
1st – Registration week
2nd	Introduction	Vehicle and sensor kit demonstration ( videos of 2023)
3rd	Estimation theory: inhomogeneous linear systems	OpenCV installation, GUI by OpenCV
4th	Lagrange multipliers; homogeneous linear systems, point-line/plane distances	Affine Transformations
5th	Random Sampling Consensus (RANSAC)	RANSAC (Whiteboard)
6th	Optimal Plane Fitting; SVD; Camera models	Pointset visualization (Whiteboard)
7th	Back-projection. Introduction to homographies.	Multi-model fitting
8th	Homography Estimation. Data Normalization.	National holiday (break)
9th – fall break
10th	Camera Calibration, RQ-decomposition.	Homography Estimation
11th	Introduction to Stereo Vision	Point registration: theory & source code
12th	Stereo vision (cont)	Tomasi-Kanade factorization (theory, source: Win, Linux)
13th	Stereo vision (cont)	Camera Calibration
14th	Planar motion Numerical optimization	Stereo pipeline
15th	Bundle adjustment. Special hardwares.	(sources in Canvas)

Oral exams:

• 18th December 11:00
• 3rd January 10:00 (room 0-409, South Building)
• 7th January 10:00 (0-412 Rényi)
• 17th January 10:00 (0-412 Rényi)
• 24th January 10:00 (0-412 Rényi)
• 31th January 16:00 (0-412 Rényi)

Assignment presentations:

• 16th December 16:00 (online)
• 2nd January 16:00 (online)
• 6th January 16:00 (online)
• 16th January 16:00 (online)
• 23rd January 16:00 (online)
• 31th January 16:00 (in person, during the oral exam)

Topics for oral exams

1. Basic estimation theory: solution for homogeneous and inhomogeneous linear system of equations.
2. Robust fitting methods: the RANSAC method.
3. Multi-model fitting: sequential RANSAC, MultiRANSAC
4. Camera models: perspective camera, orthogonal projection, weak-perspective camera.
5. Calibration of perspective camera using a spatial calibration object: Estimation and decomposition of projection matrix.
6. Chessboard-based calibration of perspective cameras. Radial and tangential distortion.
7. Plane-plane homography. Panoramic images by homographies.
8. Estimation of homography. Data normalization.
9. Basics of epipolar geometry. Essential and fundamental matrices. Rectification.
10. Estimation of essential and fundamental matrices. Data normalization. Decomposition of essential matrix.
11. Triangulation for both standard and general stereo vision.
12. Stereo vision for planar motion.
13. Tomasi-Kanade factorization: multi-view reconstruction by orthogonal and weak-perspective camera models.
14. Reconstruction by merging stereo reconstructions. Registration of two point sets by a similarity transformation.
15. Numerical optimization
16. Numerical multi-view reconstruction: bundle adjustment.
17. Tomasi-Kanade factorization with missing data.
18. Reconstruction by special devices: laser scanning, depth camera, LiDAR.