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Abstract

Real-world objects are usually composed of a number of different materials that
often show subtle changes even within a single material. Photorealistic
rendering of such objects requires accurate measurements of the reflection
properties of each material, as well as the spatially varying effects. We
present an image-based measuring method that robustly detects the different
materials of real objects and fits an average bidirectional reflectance
distribution function (BRDF) to each of them. In order to model 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 spatially varying BRDF
representation. Real-world objects often also have fine geometric detail that
is not represented in an acquired mesh. To increase the detail, we derive
normal maps even for non-Lambertian surfaces using our measured BRDFs. A high
quality model of a real object can be generated with relatively little input
data. The generated model allows for rendering under arbitrary viewing and
lighting conditions and realistically reproduces the appearance of the original
object.