16-822, Geometry based Methods in Vision, Fall 2022, Carnegie Mellon University
Chenhao Yang
Key note: For students currently enrolled in this course using any of these code directly may be considered against the academic integrity of CMU.
The course focuses on the geometric aspects of computer vision: The geometry of image formation and its use for 3D reconstruction and calibration. The objective of the course is to introduce the formal tools and results that are necessary for developing multi-view reconstruction algorithms. The fundamental tools introduced study affine and projective geometry, which are essential to the development of image formation models. These tools are then used to develop formal models of geometric image formation for a single view (camera model), two views (fundamental matrix), and three views (trifocal tensor); 3D reconstruction from multiple images; auto-calibration; and learning based methods. In particular, this course will cover topics including -
• Fundamentals of projective, affine, and Euclidean geometries • Projective Transforms in 2D and 3D • Single view geometry: The pinhole model • 2-view geometry: The Fundamental matrix • 2-view reconstruction • N-view reconstruction • Self-calibration • Learning-based SfM and SLAM