Tapping motion blur for robust normal estimation of planar scenes

We propose a framework for robust estimation of normal of a planar scene from a single motion blurred observation. We first reveal how feature points can be extracted from blur kernels and matched to generate several point correspondences. Although these points correspond to different homographies, the fact that they conform to the same normal yields a rank-3 constraint which we harness within a hierarchical clustering framework to estimate the normal accurately.