Three-Dimensional Computer Vision

Data is displayed for academic year: 2023./2024.

Lecturers

Prof.
Ivan Marković
Prof.
Tomislav Pribanić
Prof.
Siniša Šegvić

Laboratory exercises

Karlo Koledić
univ. mag. ing. el. techn. inf.
Ivan Sabolić
univ. mag. ing.

Course Description

The course studies basics of threedimensional computer vision. In particular, we consider pasive stereoscopic reconstruction, reconstruction withh structured light and specifics of reconstruction with LIDAR sensors.

Study Programmes

University graduate
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Learning Outcomes

  1. Explain fundametal elements of the geometry of one, two or more views.
  2. Explain advantages of reconstruction techniques based on pasive stereoscopy.
  3. Explain advantages of reconstruction based on active illumination.
  4. Explain advantages of reconstruction based on LIDAR sensors.
  5. Explain principles of camera ego motion estimation.

Forms of Teaching

Lectures

The course does not offer lectures in English.

Laboratory

One exercise in each half of the semester.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 50 % 20 % 50 % 0 %
Mid Term Exam: Written 0 % 40 % 0 %
Final Exam: Written 0 % 40 %
Exam: Written 50 % 80 %
Exam: Oral 20 %

Week by Week Schedule

  1. Image formation, calibration, vanishing points
  2. homography, essential matrix
  3. Feature detection, differential tracking, wide-baseline matching.
  4. Calibrated stereo: rectification, geometry, SGM, metric embeddings
  5. active 3D reconstruction: time of flight, laser scanners, structured light
  6. Structured light: projector/camera calibration, patterns for static scenes, patterns for dynamic scenes
  7. Multiview 3D reconstruction with structured light 1: classic methods of coarse and fine 3D point cloud registration
  8. Midterm exam
  9. Multiview 3D reconstruction with structured light 2: deep models for 3D point cloud registration
  10. Basics of 3D geometry and transformations, rotation and transformation matrices, rotation parameterizations, properties.
  11. Point cloud preprocessing, normal estimation, base plane estimation, 3D features and descriptors, registration (point-to-point, point-to-plane), ICP, RANSAC
  12. Effective point cloud representations, point reduction, projections, octree, voxels, kd-tree
  13. Odometry for autonomous robots and vehicles, odometry from 3D point clouds
  14. Exercises
  15. Final exam

Literature

Richard Szeliski (2010.), Computer Vision, Springer
Richard Hartley, Andrew Zisserman (2004.), Multiple View Geometry in Computer Vision, Cambridge University Press
Yi Ma, Stefano Soatto, Jana Kosecka, S. Shankar Sastry (2005.), An Invitation to 3-D Vision, Springer Science & Business Media

For students

General

ID 223691
  Winter semester
5 ECTS
L1 English Level
L1 e-Learning
30 Lectures
0 Seminar
0 Exercises
8 Laboratory exercises
0 Project laboratory
0 Physical education excercises

Grading System

89 Excellent
76 Very Good
63 Good
50 Sufficient

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