Depth Recovery of Complex Surfaces from Texture-less Pair of Stereo Images

Abstract

In this paper, a novel framework is presented to recover the 3-D shape information of a complex surface using its texture-less pair of stereo images. First a linear and generalized Lambertian model is proposed to obtain SfS data using an image from stereo pair. Then this SfS data is corrected by integrating SIFT indexes. These SIFT indexes are defined by means of disparity between the matching points (scale invariant features) in rectified stereo images. The integration process is based on correcting the 3-D visible surfaces obtained from SfS using these SIFT indexes. The SIFT indexes based improvement of depth values which are obtained from generalized Lambertian reflectance model is performed by a feed-forward neural network. The experiments are performed to demonstrate the usability and accuracy of the proposed framework.