The course Basics of 3D image processing:Acquisition and processing of 3D data is dedicated to technical approaches for the acquisition of depth information, the necessary data processing aspects. The focus is on optical approaches for 3D data acquisition, the corresponding system-technical realizations, the necessary theoretical basics as well as methods / procedures of (image) data processing.
Possible application areas of these techniques are nowadays very diverse and widespread, e.g. Computer graphics modelling of three-dimensional objects (reverse engineering), distance measurements in self-driving vehicles or for driver assistance, surface inspections or tests for dimensional accuracy in quality assurance, position estimates or obstacle localization in robotics or safety engineering. Methods for shape reconstruction contain elements and techniques of classical image processing to a large extent. In the same way, 3D aspects have to be increasingly taken into account nowadays to fulfill recognition tasks with monocular image processing.
The main image processing aspects for obtaining 3D information (3D imaging) will be discussed in the lecture in an approach-oriented manner. The detailed presentation of the classical method of stereo and multi-camera vision is complemented by current approaches such as white-light interferometry, focus variation, LIDAR or the time-of-flight principle. The basic part of the course includes important system-technical, optical and geometrical laws of image acquisition processes as well as basic principles of projective geometry.
The student receives a comprehensive overview of procedures for the reconstruction of object surfaces up to the qualitative analysis of selected scene/object points in three-dimensional scenes. In addition, the theoretical foundations, the system-technical aspects and the methods / procedures for deriving spatial, geometric scene information from digital images are discussed.
Based on the imparted contents the student is able to use his knowledge in concrete applications in one of the above mentioned fields or can use it in the context of further lectures on applied image processing, e.g.
as well as on image-based pattern recognition / artificial intelligence.
The course is accompanied by exercises and excursions in which lecture content is reviewed and discussed in depth.
