Three-Dimensional Computer Vision
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.
Learning Outcomes
- Explain advantages of reconstruction techniques based on pasive stereoscopy.
- Explain advantages of reconstruction based on active illumination.
- Explain advantages of reconstruction based on LIDAR sensors.
Forms of Teaching
Lectures
The course does not offer lectures in English.
LaboratoryOne exercise in each half of the semester.
Grading Method
Continuous Assessment | Exam | |||||
---|---|---|---|---|---|---|
Type | Threshold | Percent of Grade | Threshold | Percent of Grade | ||
Laboratory Exercises | 40 % | 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
- Image formation, calibration, vanishing points
- homography, essential matrix
- Feature detection, differential tracking, wide-baseline matching.
- Calibrated stereo: rectification, geometry, SGM, metric embeddings
- active 3D reconstruction: time of flight, laser scanners, structured light
- Structured light: projector/camera calibration, patterns for static scenes, patterns for dynamic scenes
- Multiview 3D reconstruction with structured light 1: classic methods of coarse and fine 3D point cloud registration
- Midterm exam
- Multiview 3D reconstruction with structured light 2: deep models for 3D point cloud registration
- Basics of 3D geometry and transformations, rotation and transformation matrices, rotation parameterizations, properties.
- Point cloud preprocessing, normal estimation, base plane estimation, 3D features and descriptors, registration (point-to-point, point-to-plane), ICP, RANSAC
- Effective point cloud representations, point reduction, projections, octree, voxels, kd-tree
- Odometry for autonomous robots and vehicles, odometry from 3D point clouds
- Exercises
- Final exam
Study Programmes
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Literature
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
Grading System
89 Excellent
76 Very Good
63 Good
50 Acceptable