A Statistical Analysis of Visual Cues for Estimating Dense Range Maps

A method for recovering dense range maps from sparse range maps by using statistical analysis of visual cues is presented. The proposed technique is based on constructing a 3D map of a real environment, which in turn, requires visual information to densely cover the environment to be placed. Moreover, the method relies only on the information coming from intensity images taken at the scene in question and compared to existing work, use a small, but representative, set of visual cues to estimate their geometry. The steps for implementing the proposed technique require obtaining an initial (sparse) geometric information from stereo vision. A set of visual characteristics with relevant geometric information is extracted by statistically analyzing small patches from data. These characteristics help to assign confidence values to the sparse range map and apply a range synthesis algorithm based on a Markov field model to estimate a complete dense range map. Preliminary experimental results validate the proposed method.