Patch-based sparse reconstruction of material BTFs

Abstract

We propose a simple and efficient method to reconstruct materials’ bidirectional texture functions (BTFs) from angularly sparse measurements. The key observation is that materials of similar types exhibit both similar surface structure and reflectance properties. We exploit this by manually clustering an existing database of fully measured material BTFs and fitting a linear model to each of the clusters. The models are computed not on per-texel data but on small spatial BTF patches we call apparent BTFs. Sparse reconstruction can then be performed by solving a linear least-squares problem without any regularization, using a per-cluster sampling strategy derived from the models. We demonstrate that our method is capable of faithfully reconstructing fully resolved BTFs from sparse measurements for a wide range of materials.