This paper proposes a novel methodology for generating 3D point clouds of good accuracy from stereo pairs. Initially, the methodology defines some conditions for the proper selection of image pairs. Then, the selected stereo images are used to estimate dense correspondences using the Daisy descriptor. An efficient two-phase strategy to remove outliers is then introduced. Finally, the 3D point cloud is refined by combining sub-pixel accuracy correspondences estimation and the moving least squares algorithm. The proposed methodology can be exploited by multiview stereo algorithms due to its good accuracy and its fast computation.