Cloth is essential to our everyday lives; consequently, visualizing and rendering cloth has been an important area of research in graphics for decades. One important aspect contributing to the rich appearance of cloth is its complex 3D structure. Volumetric algorithms that model this 3D structure can correctly simulate the interaction of light with cloth to produce highly realistic images of cloth. But creating volumetric models of cloth is difficult: writing specialized procedures for each type of material is onerous, and requires significant programmer effort and intuition. Further, the resulting models look unrealistically “perfect” because they lack visually important features like naturally occurring irregularities.
This paper proposes a new approach to acquiring volume models, based on density data from X-ray computed tomography (CT) scans and appearance data from photographs under uncontrolled illumination. To model a material, a CT scan is made, yielding a scalar density volume. This 3D data has micron resolution details about the structure of cloth but lacks all optical information. So we combine this density data with a reference photograph of the cloth sample to infer its optical properties. We show that this approach can easily produce volume appearance models with extreme detail, and at larger scales the distinctive textures and highlights of a range of very different fabrics such as satin and velvet emerge automatically—all based simply on having accurate mesoscale geometry.