Martin Rump

The image-based acquisition of complex optical material properties is one of the major research topics in our group. The goal of this project is the development of novel techniques for the efficient and high-fidelity capture of high-dimensional material representations like, e.g. the bidirectional texture function (BTF). Example data is publicy available at the BTF database Bonn.

Car paint and especially metallic or pearlescent paints pose serious challenges to computer graphics. This is due to their high dynamic range, their high frequent changes of reflectance both in angular and in spatial domain as well as the angular dependent color shift behaviour of pearlescent paints which is not covered by commonplace reflectance models. In the Car Paint Project we develop new compression, rendering and editing techniques for all kinds of car paints.

To correctly simulate materials under arbitrary illumination, the light simulation in a virtual scene must be calculated on a pure spectral basis. This is already done in modern rendering systems. For a few classes of materials spectral reflectance data is already acquired for a few light and view directions using spectrometers and gonioreflectometer setups. This is sometimes enough to fit analytical models to the measured data. But for anisotropic materials or for materials with strong variations in angular or spatial domain there are currently no measurement setups at hand. Similar setups like the ones based on RGB CCD cameras are impractical for spectral measurements because of the high costs of cameras and light sources needed for spectral measurements. In this project we plan to combine RGB and spectral measurement methods to come up with an efficient and pratical measurement setup for spectral BTFs. Furthermore, algorithm for analysis, compression and efficient rendering for such RGB-spectral-combined data will be investigated.