Evaluation of stereo algorithms for 3D object recognition

This work aims at evaluating stereo matching algorithms in a 3D object recognition scenario, wherein objects have to be found and their 3D pose estimated efficiently and in presence of clutter and occlusions. Unlike all other surveys and evaluations of stereo methods, which compare accuracy based on a dataset of disparity maps with ground-truth, this work proposes an evaluation in terms of recognition ability which leverages on state-of-the-art approaches for 3D object recognition. The proposed evaluation methodology comprises a novel dataset characterized by realistic working conditions and compares state-of-the-art stereo algorithms potentially suitable to 3D object recognition applications.