Image-Based Reconstruction of Shift-Variant Materials

towards photorealistic rendering. Many real-world objects are composed
of a number of materials which often show subtle changes even within a
single material. Thus, for photorealistic rendering both the general
surface properties as well as the spatially varying effects of the
object are needed.

We present an image-based measuring method that robustly detects the
different materials of real objects and fits an average bidirectional
reflectance distribution functions (BRDF) to each of them. In order to
model the local changes as well, we project the measured data for each
surface point into a basis formed by the recovered BRDFs leading to a
truly shift-variant BRDF representation.

A high quality model of a real object can be generated with relatively
few input data. The generated model allows for rendering under
arbitrary viewing and lighting conditions and realistically reproduces
the appearance of the original object.