Recovering Spatially Varying Reflectance from a Sparse Set of Images

从一组稀疏图像中估计物体表面的照度和反射率是一个重要但固有不适定的问题。如果我们想要解释表面材料属性的空间变化，这个问题就变得更加困难了。在这项研究中，我们推导了一种新的方法来估计空间变化的镜面反射特性，已知几何形状的表面，以及来自镜面图像的照明分布，例如，使用偏振来分离反射分量。与以前的工作不同，我们没有假设照明是单点光源。我们用球面统计分布对镜面反射进行建模，并用其参数值的径向基函数网络对其空间变化进行编码。这使我们能够制定同时估计空间变化的镜面反射率和照明，作为基于i -散度测量的约束优化。为了解决这个问题，我们推导了一个期望最大化算法。同时，我们通过学习rbf的数量、中心和宽度，完成了镜面反射特性的优化编码。

Estimating the illumination and the reflectance properties of an object surface from a sparse set of images is an important but inherently ill-posed problem. The problem becomes even harder if we wish to account for the spatial variation of material properties on the surface. In this research, we derived a novel method for estimating the spatially varying specular reflectance properties, of a surface of known geometry, as well as the illumination distribution from a specular-only image, for instance, captured using polarization to separate reflection components. Unlike previous work, we did not assume the illumination to be a single point light source. We modeled specular reflection with a spherical statistical distribution and encoded its spatial variation with radial basis function network of their parameters values. This allowed us to formulate the simultaneous estimation of spatially varying specular reflectance and illumination as a constrained optimization based on the I-divergence measure. To solve it, we derived an expectation maximization algorithm. At the same time, we accomplished the optimum encoding of the specular reflectance properties by learning the number, centers and widths of the RBFs.