CENG 608
3D Photography
This course covers algorithms and applications to extract 3D information (especially shape) from images. It starts with the camera model and calibration, 2D and 3D projective geometries and extracting feature points. Then it covers passive 3D reconstruction techniques such as single view reconstruction, structure from motion, shape from silhouettes. Active sensing techniques (time of flight cameras, structured light, laser scanners etc.) that directly obtain 3D data are also briefly covered.
Course Objectives
To teach the imaging geometry of cameras. To describe the algorithms used in 3D information and shape extraction and to improve ability to implement these algorithms. To form acquaintance with the applications of this research area. To improve the ability of written and oral presentation.
Recommended or Required Reading
Computer Vision: Algorithms and Applications, Richard Szeliski, Springer, 2010.
Learning Outcomes:
1. Describe the fundamental concepts in imaging geometry
2. Identify state-of-the-art techniques used in 3D information and shape extraction from images
3. Construct programs that implement algorithms of 3D information extraction and 3D reconstruction
4. Prepare written and oral presentation for the performed work
Topics |
Camera models and calibration |
Feature point extraction and matching |
Feature point tracking and optical flow |
2D projective geometry |
RANSAC and image alignment using feature points (image warping) |
3D projective geometry and epipolar geometry |
Two-view and multi-view structure-from-motion |
3D simultaneous localization and mapping |
Depth extraction with stereo matching |
Image segmentation |
Shape-from-X |
3D reconstruction |
Active sensing techniques |
Grading
Midterm 25%
Homework 15%
Presentation 30%
Final 30%
Instructor(s)
- CENG 500
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