Image space acquisition and modelling
In order to achieve realistic images, computer graphics employs detailed models which accurately describe the geometry and the materials of the scene to be rendered. The creation of those models comes at a high cost, though, especially when they need to be produced manually. This lead to the development of acquisition techniques which construct these models for real-world scenes automatically.
Image space methods stand out in that they do not require explicit scene descriptions as collections of surfaces and their illumination response. Instead, they capture a set of images of the scene, and render the desired result image directly out of that. Thus, they are especially suited for complex scene geometries and can robustly handle a multitude of materials – albeit at the cost of requiring large collections of input images for exact results.
|Global illumination effects, such as reflections, refractions and shadows are handled intrinsically correctly for a multitude of materials by image space relighting. This picture shows a rendering of a test scene assembled from only 230 input pictures
Image Space Relighting
|From left to right: the glass mouse has a geometry which is hard to understand even for a human observer if only a single view is available (the photo shows the original). FIRS allows to virtually cut through the object and construct a data set which can be visualized as a volume, but also be assembled to a surface model.