Refine
Year of publication
Document Type
- Conference Proceeding (642)
- Article (426)
- Other Publications (143)
- Part of a Book (141)
- Working Paper (128)
- Book (118)
- Report (115)
- Journal (Complete Issue of a Journal) (85)
- Master's Thesis (77)
- Doctoral Thesis (58)
Language
- German (1113)
- English (882)
- Multiple languages (8)
Keywords
Institute
- Fakultät Architektur und Gestaltung (41)
- Fakultät Bauingenieurwesen (104)
- Fakultät Elektrotechnik und Informationstechnik (34)
- Fakultät Informatik (121)
- Fakultät Maschinenbau (60)
- Fakultät Wirtschafts-, Kultur- und Rechtswissenschaften (106)
- Institut für Angewandte Forschung - IAF (115)
- Institut für Naturwissenschaften und Mathematik - INM (3)
- Institut für Optische Systeme - IOS (39)
- Institut für Strategische Innovation und Technologiemanagement - IST (60)
Using multi-camera matching techniques for 3d reconstruction there is usually the trade-off between the quality of the computed depth map and the speed of the computations. Whereas high quality matching methods take several seconds to several minutes to compute a depth map for one set of images, real-time methods achieve only low quality results. In this paper we present a multi-camera matching method that runs in real-time and yields high resolution depth maps. Our method is based on a novel multi-level combination of normalized cross correlation, deformed matching windows based on the multi-level depth map information, and sub-pixel precise disparity maps. The whole process is implemented completely on the GPU. With this approach we can process four 0.7 megapixel images in 129 milliseconds to a full resolution 3d depth map. Our technique is tailored for the recognition of non-technical shapes, because our target application is face recognition.