Bibliography | Maurer, Daniel: Depth-Driven Variational Methods for Stereo Reconstruction. University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 3 (2014). 73 pages, english.
|
CR-Schema | I.2.10 (Vision and Scene Understanding) I.4.8 (Image Processing and Computer Vision Scene Analysis)
|
Abstract | Stereo reconstruction belongs to the fundamental problems in computer vision, with the aim of reconstructing the depth of a static scene. In order to solve this problem the corresponding pixels in both views must be found. A common technique is to minimize an energy (cost) function. Therefore, most methods use a parameterization in form of a displacement information (disparity). In contrast, this thesis uses, extends and examines a depth parameterization. (i) First a basic depth-driven variational method is developed based on a recently presented method of Basha et al. [2]. (ii) After that, several possible extensions are presented, in order to improve the developed method. These extensions include advanced smoothness terms that incorporate image information and enable an anisotropic smoothing behavior. Further advanced data terms are considered, which use modified constraints to allow a more accurate estimation in different situations. (iii) Finally, all extensions are compared with each other and with a disparity-driven counterpart.
|
Full text and other links | PDF (14237551 Bytes)
|
Department(s) | University of Stuttgart, Institute of Visualisation and Interactive Systems, Visualisation and Interactive Systems
|
Superviser(s) | Bruhn, Andrés |
Entry date | December 2, 2014 |
---|