Nowadays we are experiencing a resurgence of interest in the traditional methods of Computer Vision. While the dazzling success of deep learning has inspired the research throughout the last decade, the geometry of vision has never ceased to be a fertile ground for investigation.
This tutorial offers a self-contained overview of multi-view geometry, an essential tool in Computer Vision that is at the core of several classical and modern algorithms involving a variety of applications, including 3D reconstruction, motion segmentation, visual SLAM and augmented reality.

Aimed primarily at an audience unfamiliar with these topics, this tutorial will guide the attendees on a tour that starts from the geometric foundations of 3D vision to reach up some of the research challenges that are still open in the field.
The course will introduce classical topics such as camera models, camera calibration, the epipolar geometry, and then move to multi-view relations. We will also address the problem of robust estimation and structure from motion. Special emphasis will be put on rigorous mathematical formulations and on the practical aspects that separate theory from efficient and working implementations. 

Topics

1. Introduction

2. Rudiments of projective geometry 

3. Single-view geometry

• camera model

• camera calibration

4. Two-view geometry

• epipolar geometry

• robust model fitting

5. Structure from motion

1. • multi-view geometry

   • bundle adjustment

   • taxonomy of approaches

   • theoretical assumptions

6. Concluding remarks & active research areas

Materials

TBD

Schedule

TBD

Organizers