Blue noise Sampling of surfaces from stereoscopic images

Abstract

We propose an original technique to sample surfaces generated by stereoscopic acquisition systems. Our motivation is to simplify the long and fastidious sampling pipeline, for such acquisition systems. The idea is to make the sampling of the surfaces directly on the pair of stereoscopic images, instead of doing it on the meshes created by triangulation of the point clouds given by the acquisition system. More precisely, we present a feature-preserving sampling, done directly in the stereoscopic image domain, while computing the inter-sample distances in the 3D space, in order to reduce the distortion due the embedding in R3. We focus on Poisson-disk sampling, because of its nice blue noise properties. Experimental results show that our method is a good trade-off between the direct sampling methods that are time-consuming, and the methods based on parameterization that alter the final sampling properties.