3D Scanning

Data is displayed for the academic year: 2024./2025.

Lecturers

Assoc. Prof.
Tomislav Petković
PhD

Course Description

In this course students gain knowledge about 3D scanning.
The following topics are covered: Introduction to 3D scanning and profilometry. Image formation model. Perspective projection. Decomposition of the perspective projection matrix. Intrinsic and extrinsic parameters. Binocular vision. Stereo camera pair. Epipolar constraint. Stereo rectification. Fundamental matrix. Triangulation and 3D reconstruction. Correspondence problem. Keypoints. RANSAC. Homographies. Single camera calibration. Calibration of a stereo camera pair. Autocalibration. Three views. Trifocal tensor. Geometries with more than three views. Bundle adjustment. Cheirality. Structured light. Classification of structured light patterns. One-shot and multiple-shot patterns. Light sources and projectors. Fringe projection profilometry. Phase unwrapping. Projector calibration. Systems using multiple cameras and projectors. Time-of-flight cameras. Coding functions. Point clouds. Registration. ICP algorithm. Laser 3D scanning. Handheld 3D scanners.

Prerequisites

A good background in mathematics including linear algebra, calculus and geometry.

Study Programmes

University graduate
[FER3-HR] Audio Technologies and Electroacoustics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Communication and Space Technologies - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Computational Modelling in Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Computer Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Computer Science - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Control Systems and Robotics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Data Science - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electrical Power Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electric Machines, Drives and Automation - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electronic and Computer Engineering - profile
Elective Courses (1. semester) (3. semester)
Elective Courses of the Profile (1. semester) (3. semester)
[FER3-HR] Electronics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Information and Communication Engineering - profile
Elective Courses (1. semester) (3. semester)
Elective Courses of the Profile (1. semester) (3. semester)
[FER3-HR] Network Science - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Software Engineering and Information Systems - profile
Elective Courses (1. semester) (3. semester)

Learning Outcomes

  1. List technologies which are used in 3D scanning
  2. Explain the image formation model and perspective projection
  3. Explain and perform camera calibration
  4. Compare 3D scanning methods
  5. Analyze accuracy and uncertainty of 3D reconstruction
  6. Select 3D scanning method depending on the application

Forms of Teaching

Lectures

Lectures present theoretical concepts.

Seminars and workshops

Seminar includes discussion on theoretical concepts presented during lectures.

Laboratory

Laboratory exercises facilitate better understanding of the problems of 3D scanning.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 50 % 20 % 50 % 20 %
Seminar/Project 50 % 20 % 50 % 20 %
Mid Term Exam: Written 0 % 30 % 0 %
Final Exam: Written 0 % 30 %
Exam: Written 50 % 60 %
Comment:

The mandatory prerequisites for the passing grade are at least 50% points achieved on the midterm and on the final exam combined, and at least 50% on the laboratory and on the seminar.

Week by Week Schedule

  1. Introduction to 3D scanning and profilometry. Image formation model.
  2. Perspective projection. Decomposition of the perspective projection matrix. Intrinsic and extrinsic parameters.
  3. Binocular vision. Stereo camera pair. Epipolar constraint. Stereo rectification. Fundamental matrix.
  4. Triangulation and 3D reconstruction. Correspondence problem. Keypoints. RANSAC. Homographies.
  5. Single camera calibration. Calibration of a stereo camera pair. Autocalibration.
  6. Three views. Trifocal tensor. Geometries with more than three views.
  7. Bundle adjustment. Cheirality.
  8. Midterm exam
  9. Structured light. Classification of structured light patterns. One-shot and multiple-shot patterns. Light sources and projectors.
  10. Fringe projection profilometry. Phase unwrapping.
  11. Projector calibration. Systems using multiple cameras and projectors.
  12. Time-of-flight cameras. Coding functions.
  13. Point clouds. Registration. ICP algorithm.
  14. Laser 3D scanning. Handheld 3D scanners. Other 3D scanning methods.
  15. Final exam

Literature

Richard Hartley, Andrew Zisserman (2004.), Multiple View Geometry in Computer Vision, Cambridge University Press
Pietro Zanuttigh, Giulio Marin, Carlo Dal Mutto, Fabio Dominio, Ludovico Minto, Guido Maria Cortelazzo (2016.), Time-of-Flight and Structured Light Depth Cameras, Springer
Silvio Giancola, Matteo Valenti, Remo Sala (2018.), A Survey on 3D Cameras: Metrological Comparison of Time-of-Flight, Structured-Light and Active Stereoscopy Technologies, Springer

General

ID 222434
  Winter semester
5 ECTS
L0 English Level
L1 e-Learning
30 Lectures
5 Seminar
0 Exercises
15 Laboratory exercises
0 Project laboratory
0 Physical education excercises

Grading System

87 Excellent
75 Very Good
64 Good
51 Sufficient

Similar Courses