Christian Nitschke

A Framework for Realtime 3-D Reconstruction by Space Carving Using Graphics Hardware Christian Nitschke

Diplomarbeit, die am 06.12.2006 erfolgreich an einer Universität in Deutschland im Fachbereich Medien eingereicht wurde. Introduction: Reconstruction of real-world scenes from a set of multiple images is a topic in Computer Vision and 3D Computer Graphics with many interesting applications. There is a relation to Augmented and Mixed Reality (AR/MR), Computer-Supported Collaborative Work (CSCW), Computer-Aided industrial/architectural Design (CAD), modeling of the real-world (e.g. computer games, scenes/effects in movies), entertainment (e.g. 3D TV/Video) and recognition/analyzing of real-world characteristics by computer systems and robots. There exists a powerful algorithm theory for shape reconstruction from arbitrary viewpoints, called shape from photo-consistency. However, it is computationally expensive and hence can not be used with applications in the field of 3D video or CSCW as well as interactive 3D model creation. Attempts have been made to achieve real-time framerates using PC cluster systems. While these provide enough performance they are also expensive and less flexible. Approaches that use GPU hardware-acceleration on single workstations achieve interactive framerates for novel-view synthesis, but do not provide an explicit volumetric representation of the whole scene. The proposed approach shows the efforts in developing a GPU hardware-accelerated framework for obtaining the volumetric photo hull of a dynamic 3D scene as seen from multiple calibrated cameras. High performance is achieved by employing a shape from silhouette technique in advance to obtain a tight initial volume for shape from photo-consistency. Also several speed-up techniques are presented to increase efficiency. Since the entire processing is done on a single PC, the framework can be applied to mobile setups, enabling a wide range of further applications. The approach is explained using programmable vertex and fragment processors and compared to highly optimized CPU...