Subject description - AD4M33TDV

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AD4M33TDV 3D Computer Vision
Roles:PO, V Extent of teaching:14KP+6KC
Department:13133 Language of teaching:CS
Guarantors:  Completion:Z,ZK
Lecturers:  Credits:6
Tutors:  Semester:Z


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.

Study targets:

To master conceptual and practical knowledge of the basic methods in 3D computer vision.

Course outlines:

1. 3D computer vision, goals and applications, the course overview
2. Real perspective camera
3. Calibration of real perspective camera
4. Epipolar geometry
5. Computing camera matrices and 3D points from sparse correspondences
6. Autocalibration
7. Consistent multi-camera reconstruction
8. Optimal scene reconstruction
9. Epipolar image rectification
10. Stereoscopic vision
11. Algorithms for binocular stereoscopic matching, multi-camera
12. Shape from shading and contour
13. Shape from texture, defocus, and color
14. Surface reconstruction

Exercises outline:

1. Labs introduction and overview, experimental data, entrance test
2. Camera calibration without radial distortion from a known scene
3. Camera calibration with radial distortion from a known scene
4. Computing epipolar geometry from 8 points
5. Computing epipolar geometry from 7 points, RANSAC
6. Constructing projection matrices from epipolar geometry, computing camera motion and scene structure
7. Autocalibration of intrinsic camera parameters
8. Consistent reconstruction of a many-camera system
9. Accuracy improvement by bundle adjustment
10. Time slot to finish all pending assignments
11. Epipolar rectification for stereoscopic vision
12. Stereoscopic matching by dynamic programming
13. 3D point cloud reconstruction
14. 3D sketch reconstruction


R. Hartley and A. Zisserman. Multiple View Geometry. 2nd ed. Cambridge
University Press 2003.
Y. Ma, S. Soatto, J. Kosecka, S.S. Sastry. An Invitation to 3D
Vision. Springer 2004.


Knowledge equivalent to Geometry for Computer Vision and Graphics and Computer Vision Methods. Detailed up-to-date information on the course at


computer vision, digital image and video processing

Subject is included into these academic programs:

Program Branch Role Recommended semester
MKEEM1 Technological Systems V 3
MKEEM5 Economy and Management of Electrical Engineering V 3
MKEEM4 Economy and Management of Power Engineering V 3
MKEEM3 Electrical Power Engineering V 3
MKEEM2 Electrical Machines, Apparatus and Drives V 3
MKKME1 Wireless Communication V 3
MKKME5 Systems of Communication V 3
MKKME4 Networks of Electronic Communication V 3
MKKME3 Electronics V 3
MKKME2 Multimedia Technology V 3
MKOI3 Computer Vision and Image Processing PO 3
MKKYR4 Aerospace Systems V 3
MKKYR1 Robotics V 3
MKKYR3 Systems and Control V 3
MKKYR2 Sensors and Instrumentation V 3

Page updated 3.7.2020 17:51:56, semester: Z,L/2020-1, L/2019-20, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)