We develop a linear algebraic framework for the shape-from-shading problem, because tensors arise when scalar (e.g. image) and vector (e.g. surface normal) fields are differentiated multiple times. The work is in two parts. In this first part we investigate when image derivatives exhibit invariance to changing illumination by calculating the statistics of image derivatives under general distributions on the light source. We computationally validate the hypothesis that image orientations (derivatives) provide increased invariance to illumination by showing (for a Lambertian model) that a shape-from-shading algorithm matching gradients instead of intensities provides more accurate reconstructions when illumination is incorrectly estimated under a flatness prior.
@article{arxiv.1705.05885,
title = {What's In A Patch, I: Tensors, Differential Geometry and Statistical Shading Analysis},
author = {Daniel Niels Holtmann-Rice and Benjamin S. Kunsberg and Steven W. Zucker},
journal= {arXiv preprint arXiv:1705.05885},
year = {2017}
}