A Speeded-Up Local Descriptor for dense stereo matching

This paper presents a novel local image descriptor for dense wide-baseline matching purposes, coined SULD (speeded-up local descriptor). SULD approximates or even outperforms than previously proposed schemes such as SURF and DAISY, and can be computed and compared much faster. This is achieved by summing up the Haar wavelet responses rather than the gradient, by computing convolutions recursively and by using low dimensions descriptor. The proposed approach was tested with ground truth laser scanned depth maps as well as on image pairs of different resolutions and the results show that good reconstruction is achieved even with only two small images.