| dc.description.abstract | The creation of virtual content from visual data is a tedious task which requires a high
amount of skill and expertise. Although the majority of consumers is in possession of
multiple imaging devices that would enable them to perform this task in principle, the
processing techniques and tools are still intended for the use by trained experts. As
more and more capable hardware becomes available, there is a growing need among
consumers and professionals alike for new flexible and reliable tools that reduce the
amount of time and effort required to create high-quality content.
This thesis describes advances of the state of the art in three areas of computer vision:
camera motion estimation, probabilistic 3D reconstruction, and template fitting.
First, a new camera model geared towards stereoscopic input data is introduced,
which is subsequently developed into a generalized framework for constrained camera
motion estimation. A probabilistic reconstruction method for 3D line segments is then
described, which takes global connectivity constraints into account. Finally, a new
framework for symmetry-aware template fitting is presented, which allows the creation
of high-quality models from low-quality input 3D scans.
Evaluations with a broad range of challenging synthetic and real-world data sets
demonstrate that the new constrained camera motion estimation methods provide
improved accuracy and flexibility, and that the new constrained 3D reconstruction
methods improve the current state of the art. | en_US |
