In recent years, the consideration of solely static scenes, which is commonly the case for light fields, was extended more and more to dynamic scenes. The work on reconstruction of light fields from scenes which contain moving but rigid objects was begun already in 2003 and was continued in 2005 as well. After the segmentation of features in "background" and "moving object", the two 3D reconstructions, which have been estimated independently from each other, are now registered with sufficient accuracy. From this, the object's own motion is calculated, and similar object positions are assigned to one respective time step using vector quantization. The visualization of the resulting dynamic light field is done by so-called confidence maps, which allow the masking of the moving object for time steps which are currently not visible.

A newly developed technique for evaluating the quality of a light field which calculates the signal-to-noise ratio (SNR) allows an objective comparison of different light field reconstruction procedures. It was already employed for evaluating the estimation of intrinsic camera parameters and for a comparison of different procedures for calculating depth information. At the same time, local proxies, i.e., triangle meshes which are valid locally for each image and which were applied to light field visualization for the first time, were compared to conventional depth maps. In addition to that, SNR was used to optimize the depth maps of an existing light field by feeding back information from light field synthesis.

The applicability of the methods developed was shown in several cooperations with other projects. In cooperation with the Graduate Research Center "3D Image Analysis and Synthesis" the 3D reconstruction algorithms were applied for fast training in object recognition. In subproject A7 of the Sonderforschungsbereich 603 these algorithms were used for 3D reconstruction of image sequences which were recorded using flexible endoscopes.