Photogrammetric Computer Vision

Die Lehrveranstaltung gibt eine Einführung in die Grundlagen der Sensororientierung und 3D-Rekonstruktion. Das Ziel ist ein Verständnis der Prinzipien, Methoden und Anwendungen der bildbasierten Vermessung. Behandelt werden unter anderem die algebraische projektive Geometrie, Abbildungsgeometrie, Kalibrierung, Orientierungsverfahren, Stereo-Bildzuordnung und weitere Verfahren zur Oberflächenrekonstruktion.

Bitte beachten Sie: Die Unterlagen zu unseren Vorlesungen und Übungen sind nur aus dem Netzwerk der Bauhaus-Universität Weimar erreichbar.

Vorlesung

Introduction

  1. Administration, image-based 3D reconstruction, historical notes, classification of photogrammetry, field of application (slides, print)

Projective 2D geometry

  1. General mathematics, projective geometry, homogeneous 2D coordinates, points and lines (slides, print)
  2. Normalization and interpretation, matrix algebra, planar transformations, concatenation (slides, print)
  3. Estimation of projectivity, 2D homography computation, linear homogeneous equation systems, planar rectification (slides, print)

Projective 3D geometry

  1. Points and planes, spatial transformations, geometric camera model (slides, print)
  2. Projection matrix, interpretation, spatial resection/DLT, optical imaging with lenses (slides, print)
  3. Lens distortion, imaging techniques, ...

Projective multi-view geometry

  1. Relative orientation, epipolar geometry (slides, print)
  2. Fundamental matrix, spatial intersection, projective reconstruction (slides, print)
  3. Essential matrix, normal case, trifocal geometry, trifocal and multi-view tensors (slides, print)
  4. Tensor notation, bundle adjustment (slides, print)
  5. Euclidean and metric upgrade, auto-calibration, ... 

Stereo image matching

  1. Robust parameter estimation, M-estimators, RANSAC (slides, print)
  2. Correspondence problem, image matching constraints, area-based image matching, similarity measures (slides, print)
  3. Least-squares matching, global matching strategies, normal image generation (slides, print)
  4. Summary (slides, print)

Übung

The exercises will take place biweekly on Wednesdays (13:30 - 15:00)  in lecture hall 15 (SR 015) in Bauhausstraße 11. For relevant study materials, exercise sheets and assignment submissions, please visit the Moodle page for Photogrammetric Computer Vision. An enrollment password will be announced during the first exercise on 21. October 2019.

Over the course of this semester, the following topics will be covered:

1. Exercise: Points and lines in the plane, first steps in MATLAB

2. Exercise: Projective transformation (Homography)

3. Exercise: Camera calibration using direct linear transformation (DLT)

4. Exercise: Orientation of an image pair

5. Exercise: Projective and direct Euclidean reconstruction

6. Exercise: Stereo image matching

Klausur

Written examination

Date: Monday, February 11, 2019 at 11:00 am in Audimax, S6 F
Auxiliary resources: none

Preparation material