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Statistical Acquisition of Texture Appearance
We propose a simple method to acquire and reconstruct material appearance with sparsely sampled data. Our technique renders elaborate view- and light-dependent effects and faithfully reproduces materials such as fabrics and knitwears. Our approach uses sparse measurements to reconstruct a full six-dimensional Bidirectional Texture Function (BTF). Our reconstruction only require input images from the top view to be registered, which is easy to achieve with a fixed camera setup. Bidirectional properties are acquired from a sparse set of viewing directions through image statistics and therefore precise registrations for these views are unnecessary. Our technique is based on multi-scale histograms of image pyramids. The full BTF is generated by matching the corresponding pyramid histograms to interpolated top-view images. We show that the use of multi-scale image statistics achieves a visually plausible appearance. However, our technique does not fully capture sharp specularities or the geometric aspects of parallax. Nonetheless, a large class of materials can be reproduced well with our technique, and our statistical characterization enables acquisition of such materials efficiently using a simple setup.